Path: bloom-beacon.mit.edu!hookup!news.moneng.mei.com!howland.reston.ans.net!xlink.net!ira.uka.de!prechelt From: prechelt@ira.uka.de (Lutz Prechelt) Newsgroups: comp.ai.neural-nets,comp.answers,news.answers Subject: FAQ in comp.ai.neural-nets -- monthly posting Supersedes: Followup-To: comp.ai.neural-nets Date: 28 Mar 1994 02:17:47 GMT Organization: University of Karlsruhe, Germany Lines: 2420 Approved: news-answers-request@MIT.Edu Expires: 2 May 1994 02:18:03 GMT Message-ID: Reply-To: prechelt@ira.uka.de (Lutz Prechelt) NNTP-Posting-Host: i41s25.ira.uka.de Keywords: questions, answers, terminology, bibliography Originator: prechelt@i41s25 Xref: bloom-beacon.mit.edu comp.ai.neural-nets:7859 comp.answers:4333 news.answers:16911 Archive-name: neural-net-faq Last-modified: 94/03/21 (FAQ means "Frequently Asked Questions") ------------------------------------------------------------------------ Anybody who is willing to contribute any question or information, please email me; if it is relevant, I will incorporate it. But: PLEASE format your contribution appropriately so that I can just drop it in. The monthly posting departs at the 28th of every month. ------------------------------------------------------------------------ This is a monthly posting to the Usenet newsgroup comp.ai.neural-nets (and comp.answers, where it should be findable at ANY time) Its purpose is to provide basic information for individuals who are new to the field of neural networks or are just beginning to read this group. It shall help to avoid lengthy discussion of questions that usually arise for beginners of one or the other kind. >>>>> SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION <<<<< and >>>>> DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING <<<<< This posting is archived in the periodic posting archive on host rtfm.mit.edu (and on some other hosts as well). Look in the anonymous ftp directory "/pub/usenet/news.answers", the filename is as given in the 'Archive-name:' header above. If you do not have anonymous ftp access, you can access the archives by mail server as well. Send an E-mail message to mail-server@rtfm.mit.edu with "help" and "index" in the body on separate lines for more information. For those of you who read this posting anywhere other than in comp.ai.neural-nets: To read comp.ai.neural-nets (or post articles to it) you need Usenet News access. Try the commands, 'xrn', 'rn', 'nn', or 'trn' on your Unix machine, 'news' on your VMS machine, or ask a local guru. The monthly posting is not meant to discuss any topic exhaustively. Disclaimer: This posting is provided 'as is'. No warranty whatsoever is expressed or implied, in particular, no warranty that the information contained herein is correct or useful in any way, although both is intended. >> To find the answer of question number (if present at all), search >> for the string "-A.)" (so the answer to question 12 is at "-A12.)") And now, in the end, we begin: ============================== Questions ============================== (the short forms and non-continous numbering is intended) 1.) What is this newsgroup for ? How shall it be used ? 2.) What is a neural network (NN) ? 3.) What can you do with a Neural Network and what not ? 4.) Who is concerned with Neural Networks ? 6.) What does 'backprop' mean ? 7.) How many learning methods for NNs exist ? Which ? 8.) What about Genetic Algorithms ? 9.) What about Fuzzy Logic ? 10.) Good introductory literature about Neural Networks ? 11.) Any journals and magazines about Neural Networks ? 12.) The most important conferences concerned with Neural Networks ? 13.) Neural Network Associations ? 14.) Other sources of information about NNs ? 15.) Freely available software packages for NN simulation ? 16.) Commercial software packages for NN simulation ? 17.) Neural Network hardware ? 19.) Databases for experimentation with NNs ? ============================== Answers ============================== ------------------------------------------------------------------------ -A1.) What is this newsgroup for ? The newsgroup comp.ai.neural-nets is inteded as a forum for people who want to use or explore the capabilities of Artificial Neural Networks or Neural-Network-like structures. There should be the following types of articles in this newsgroup: 1. Requests Requests are articles of the form "I am looking for X" where X is something public like a book, an article, a piece of software. The most important about such a request is to be as specific as possible! If multiple different answers can be expected, the person making the request should prepare to make a summary of the answers he/she got and announce to do so with a phrase like "Please reply by email, I'll summarize to the group" at the end of the posting. The Subject line of the posting should then be something like "Request: X" 2. Questions As opposed to requests, question ask for a larger piece of information or a more or less detailed explanation of something. To avoid lots of redundant traffic it is important that the poster provides with the question all information s/he already has about the subject asked and state the actual question as precise and narrow as possible. The poster should prepare to make a summary of the answers s/he got and announce to do so with a phrase like "Please reply by email, I'll summarize to the group" at the end of the posting. The Subject line of the posting should be something like "Question: this-and-that" or have the form of a question (i.e., end with a question mark) 3. Answers These are reactions to questions or requests. As a rule of thumb articles of type "answer" should be rare. Ideally, in most cases either the answer is too specific to be of general interest (and should thus be e-mailed to the poster) or a summary was announced with the question or request (and answers should thus be e-mailed to the poster). The subject lines of answers are automatically adjusted by the news software. Note that sometimes longer threads of discussion evolve from an answer to a question or request. In this case posters should change the subject line suitably as soon as the topic goes too far away from the one announced in the original subject line. You can still carry along the old subject in parentheses in the form "Subject: <...new subject...> (was: <...old subject...>) 4. Summaries In all cases of requests or questions the answers for which can be assumed to be of some general interest, the poster of the request or question shall summarize the ansers he/she received. Such a summary should be announced in the original posting of the question or request with a phrase like "Please answer by email, I'll summarize" In such a case, people who answer to a question should NOT post their answer to the newsgroup but instead mail them to the poster of the question who collects and reviews them. After about 5 to 20 days after the original posting, its poster should make the summary of answers and post it to the newsgroup. Some care should be invested into a summary: a) simple concatenation of all the answers is not enough: instead, redundancies, irrelevancies, verbosities, and errors should be filtered out (as good as possible) b) the answers should be separated clearly c) the contributors of the individual answers should be identifiable (unless they requested to remain anonymous [yes, that happens]) d) the summary should start with the "quintessence" of the answers, as seen by the original poster e) A summary should, when posted, clearly be indicated to be one by giving it a Subject line starting with "SUMMARY:" Note that a good summary is pure gold for the rest of the newsgroup community, so summary work will be most appreciated by all of us. (Good summaries are more valuable than any moderator ! :-> ) 5. Announcements Some articles never need any public reaction. These are called announcements (for instance for a workshop, conference or the availability of some technical report or software system). Announcements should be clearly indicated to be such by giving them a subject line of the form "Announcement: this-and-that" 6. Reports Sometimes people spontaneously want to report something to the newsgroup. This might be special experiences with some software, results of own experiments or conceptual work, or especially interesting information from somewhere else. Reports should be clearly indicated to be such by giving them a subject line of the form "Report: this-and-that" 7. Discussions An especially valuable possibility of Usenet is of course that of discussing a certain topic with hundreds of potential participants. All traffic in the newsgroup that can not be subsumed under one of the above categories should belong to a discussion. If somebody explicitly wants to start a discussion, he/she can do so by giving the posting a subject line of the form "Subject: Discussion: this-and-that" It is quite difficult to keep a discussion from drifting into chaos, but, unfortunately, as many many other newsgroups show there seems to be no secure way to avoid this. On the other hand, comp.ai.neural-nets has not had many problems with this effect in the past, so let's just go and hope... :-> ------------------------------------------------------------------------ -A2.) What is a neural network (NN) ? [anybody there to write something better? buzzwords: artificial vs. natural/biological; units and connections; value passing; inputs and outputs; storage in structure and weights; only local information; highly parallel operation ] First of all, when we are talking about a neural network, we *should* usually better say "artificial neural network" (ANN), because that is what we mean most of the time. Biological neural networks are much more complicated in their elementary structures than the mathematical models we use for ANNs. A vague description is as follows: An ANN is a network of many very simple processors ("units"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections. The design motivation is what distinguishes neural networks from other mathematical techniques: A neural network is a processing device, either an algorithm, or actual hardware, whose design was motivated by the design and functioning of human brains and components thereof. Most neural networks have some sort of "training" rule whereby the weights of connections are adjusted on the basis of presented patterns. In other words, neural networks "learn" from examples, just like children learn to recognize dogs from examples of dogs, and exhibit some structural capability for generalization. Neural networks normally have great potential for parallelism, since the computations of the components are independent of each other. ------------------------------------------------------------------------ -A3.) What can you do with a Neural Network and what not ? [preliminary] In principle, NNs can compute any computable function, i.e. they can do everything a normal digital computer can do. Especially can anything that can be represented as a mapping between vector spaces be approximated to arbitrary precision by feedforward NNs (which is the most often used type). In practice, NNs are especially useful for mapping problems which are tolerant of a high error rate, have lots of example data available, but to which hard and fast rules can not easily be applied. NNs are, at least today, difficult to apply successfully to problems that concern manipulation of symbols and memory. ------------------------------------------------------------------------ -A4.) Who is concerned with Neural Networks ? Neural Networks are interesting for quite a lot of very dissimilar people: - Computer scientists want to find out about the properties of non-symbolic information processing with neural nets and about learning systems in general. - Engineers of many kinds want to exploit the capabilities of neural networks on many areas (e.g. signal processing) to solve their application problems. - Cognitive scientists view neural networks as a possible apparatus to describe models of thinking and conscience (High-level brain function). - Neuro-physiologists use neural networks to describe and explore medium-level brain function (e.g. memory, sensory system, motorics). - Physicists use neural networks to model phenomena in statistical mechanics and for a lot of other tasks. - Biologists use Neural Networks to interpret nucleotide sequences. - Philosophers and some other people may also be interested in Neural Networks for various reasons. ------------------------------------------------------------------------ -A6.) What does 'backprop' mean ? [anybody to write something similarly short, but easier to understand for a beginner ? ] It is an abbreviation for 'backpropagation of error' which is the most widely used learning method for neural networks today. Although it has many disadvantages, which could be summarized in the sentence "You are almost not knowing what you are actually doing when using backpropagation" :-) it has pretty much success on practical applications and is relatively easy to apply. It is for the training of layered (i.e., nodes are grouped in layers) feedforward (i.e., the arcs joining nodes are unidirectional, and there are no cycles) nets. Back-propagation needs a teacher that knows the correct output for any input ("supervised learning") and uses gradient descent on the error (as provided by the teacher) to train the weights. The activation function is (usually) a sigmoidal (i.e., bounded above and below, but differentiable) function of a weighted sum of the nodes inputs. The use of a gradient descent algorithm to train its weights makes it slow to train; but being a feedforward algorithm, it is quite rapid during the recall phase. Literature: Rumelhart, D. E. and McClelland, J. L. (1986): Parallel Distributed Processing: Explorations in the Microstructure of Cognition (volume 1, pp 318-362). The MIT Press. (this is the classic one) or one of the dozens of other books or articles on backpropagation :-> ------------------------------------------------------------------------ -A7.) How many learning methods for NNs exist ? Which ? There are many many learning methods for NNs by now. Nobody can know exactly how many. New ones (at least variations of existing ones) are invented every week. Below is a collection of some of the most well known methods; not claiming to be complete. The main categorization of these methods is the distiction of supervised from unsupervised learning: - In supervised learning, there is a "teacher" who in the learning phase "tells" the net how well it performs ("reinforcement learning") or what the correct behavior would have been ("fully supervised learning"). - In unsupervised learning the net is autonomous: it just looks at the data it is presented with, finds out about some of the properties of the data set and learns to reflect these properties in its output. What exactly these properties are, that the network can learn to recognise, depends on the particular network model and learning method. Many of these learning methods are closely connected with a certain (class of) network topology. Now here is the list, just giving some names: 1. UNSUPERVISED LEARNING (i.e. without a "teacher"): 1). Feedback Nets: a). Additive Grossberg (AG) b). Shunting Grossberg (SG) c). Binary Adaptive Resonance Theory (ART1) d). Analog Adaptive Resonance Theory (ART2, ART2a) e). Discrete Hopfield (DH) f). Continuous Hopfield (CH) g). Discrete Bidirectional Associative Memory (BAM) h). Temporal Associative Memory (TAM) i). Adaptive Bidirectional Associative Memory (ABAM) j). Kohonen Self-organizing Map/Topology-preserving map (SOM/TPM) k). Competitive learning 2). Feedforward-only Nets: a). Learning Matrix (LM) b). Driver-Reinforcement Learning (DR) c). Linear Associative Memory (LAM) d). Optimal Linear Associative Memory (OLAM) e). Sparse Distributed Associative Memory (SDM) f). Fuzzy Associative Memory (FAM) g). Counterprogation (CPN) 2. SUPERVISED LEARNING (i.e. with a "teacher"): 1). Feedback Nets: a). Brain-State-in-a-Box (BSB) b). Fuzzy Congitive Map (FCM) c). Boltzmann Machine (BM) d). Mean Field Annealing (MFT) e). Recurrent Cascade Correlation (RCC) f). Learning Vector Quantization (LVQ) g). Backpropagation through time (BPTT) h). Real-time recurrent learning (RTRL) i). Recurrent Extended Kalman Filter (EKF) 2). Feedforward-only Nets: a). Perceptron b). Adaline, Madaline c). Backpropagation (BP) d). Cauchy Machine (CM) e). Adaptive Heuristic Critic (AHC) f). Time Delay Neural Network (TDNN) g). Associative Reward Penalty (ARP) h). Avalanche Matched Filter (AMF) i). Backpercolation (Perc) j). Artmap k). Adaptive Logic Network (ALN) l). Cascade Correlation (CasCor) m). Extended Kalman Filter(EKF) ------------------------------------------------------------------------ -A8.) What about Genetic Algorithms ? There are a number of definitions of GA (Genetic Algorithm). A possible one is A GA is an optimization program that starts with a population of encoded procedures, (Creation of Life :-> ) mutates them stochastically, (Get cancer or so :-> ) and uses a selection process (Darwinism) to prefer the mutants with high fitness and perhaps a recombination process (Make babies :-> ) to combine properties of (preferably) the succesful mutants. There is a newsgroup that is dedicated to the field of evolutionary computation called comp.ai.genetic. It has a detailed FAQ posting which, for instance, explains the terms "Genetic Algorithm", "Evolutionary Programming", "Evolution Strategy", "Classifier System", and "Genetic Programming". That FAQ also contains lots of pointers to relevant literature, software, other sources of information, et cetera et cetera. Please see the comp.ai.genetic FAQ for further information. ------------------------------------------------------------------------ -A9.) What about Fuzzy Logic ? [preliminary] [Who will write an introduction?] Fuzzy Logic is an area of research based on the work of L.A. Zadeh. It is a departure from classical two-valued sets and logic, that uses "soft" linguistic (e.g. large, hot, tall) system variables and a continuous range of truth values in the interval [0,1], rather than strict binary (True or False) decisions and assignments. Fuzzy logic is used where a system is difficult to model exactly (but an inexact model is available), is controlled by a human operator or expert, or where ambiguity or vagueness is common. A typical fuzzy system consists of a rule base, membership functions, and an inference procedure. Most Fuzzy Logic discussion takes place in the newsgroup comp.ai.fuzzy, but there is also some work (and discussion) about combining fuzzy logic with Neural Network approaches in comp.ai.neural-nets. For more details see (for example): Klir, G.J. and Folger, T.A., Fuzzy Sets, Uncertainty, and Information, Prentice-Hall, Englewood Cliffs, N.J., 1988. Kosko, B., Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ, 1992. ------------------------------------------------------------------------ -A10.) Good introductory literature about Neural Networks ? 0.) The best (subjectively, of course -- please don't flame me): Hecht-Nielsen, R. (1990). Neurocomputing. Addison Wesley. Comments: "A good book", "comprises a nice historical overview and a chapter about NN hardware. Well structured prose. Makes important concepts clear." Hertz, J., Krogh, A., and Palmer, R. (1991). Introduction to the Theory of Neural Computation. Addison-Wesley: Redwood City, California. ISBN 0-201-50395-6 (hardbound) and 0-201-51560-1 (paperbound) Comments: "My first impression is that this one is by far the best book on the topic. And it's below $30 for the paperback."; "Well written, theoretical (but not overwhelming)"; It provides a good balance of model development, computational algorithms, and applications. The mathematical derivations are especially well done"; "Nice mathematical analysis on the mechanism of different learning algorithms"; "It is NOT for mathematical beginner. If you don't have a good grasp of higher level math, this book can be really tough to get through." 1.) Books for the beginner: Aleksander, I. and Morton, H. (1990). An Introduction to Neural Computing. Chapman and Hall. (ISBN 0-412-37780-2). Comments: "This book seems to be intended for the first year of university education." Beale, R. and Jackson, T. (1990). Neural Computing, an Introduction. Adam Hilger, IOP Publishing Ltd : Bristol. (ISBN 0-85274-262-2). Comments: "It's clearly written. Lots of hints as to how to get the adaptive models covered to work (not always well explained in the original sources). Consistent mathematical terminology. Covers perceptrons, error-backpropagation, Kohonen self-org model, Hopfield type models, ART, and associative memories." Dayhoff, J. E. (1990). Neural Network Architectures: An Introduction. Van Nostrand Reinhold: New York. Comments: "Like Wasserman's book, Dayhoff's book is also very easy to understand". Haykin, S. (1994). Neural Networks, a Comprehensive Foundation. Macmillan, New York, NY. McClelland, J. L. and Rumelhart, D. E. (1988). Explorations in Parallel Distributed Processing: Computational Models of Cognition and Perception (software manual). The MIT Press. Comments: "Written in a tutorial style, and includes 2 diskettes of NN simulation programs that can be compiled on MS-DOS or Unix (and they do too !)"; "The programs are pretty reasonable as an introduction to some of the things that NNs can do."; "There are *two* editions of this book. One comes with disks for the IBM PC, the other comes with disks for the Macintosh". McCord Nelson, M. and Illingworth, W.T. (1990). A Practical Guide to Neural Nets. Addison-Wesley Publishing Company, Inc. (ISBN 0-201-52376-0). Comments: "No formulas at all( ==> no good)"; "It does not have much detailed model development (very few equations), but it does present many areas of application. It includes a chapter on current areas of research. A variety of commercial applications is discussed in chapter 1. It also includes a program diskette with a fancy graphical interface (unlike the PDP diskette)". Muller, B. and Reinhardt, J. (1990). Neural Networks, An Introduction. Springer-Verlag: Berlin Heidelberg New York (ISBN: 3-540-52380-4 and 0-387-52380-4). Comments: The book was developed out of a course on neural-network models with computer demonstrations that was taught by the authors to Physics students. The book comes together with a PC-diskette. The book is divided into three parts: 1) Models of Neural Networks; describing several architectures and learing rules, including the mathematics. 2) Statistical Physiscs of Neural Networks; "hard-core" physics section developing formal theories of stochastic neural networks. 3) Computer Codes; explanation about the demonstration programs. First part gives a nice introduction into neural networks together with the formulas. Together with the demonstration programs a 'feel' for neural networks can be developed. Orchard, G.A. & Phillips, W.A. (1991). Neural Computation: A Beginner's Guide. Lawrence Earlbaum Associates: London. Comments: "Short user-friendly introduction to the area, with a non-technical flavour. Apparently accompanies a software package, but I haven't seen that yet". Wasserman, P. D. (1989). Neural Computing: Theory & Practice. Van Nostrand Reinhold: New York. (ISBN 0-442-20743-3) Comments: "Wasserman flatly enumerates some common architectures from an engineer's perspective ('how it works') without ever addressing the underlying fundamentals ('why it works') - important basic concepts such as clustering, principal components or gradient descent are not treated. It's also full of errors, and unhelpful diagrams drawn with what appears to be PCB board layout software from the '70s. For anyone who wants to do active research in the field I consider it quite inadequate"; "Okay, but too shallow"; "Quite easy to understand"; "The best bedtime reading for Neural Networks. I have given this book to numerous collegues who want to know NN basics, but who never plan to implement anything. An excellent book to give your manager." Wasserman, P.D. (1993). Advanced Methods in Neural Computing. Van Nostrand Reinhold: New York (ISBN: 0-442-00461-3). Comments: Several neural network topics are discussed e.g. Probalistic Neural Networks, Backpropagation and beyond, neural control, Radial Basis Function Networks, Neural Engineering. Furthermore, several subjects related to neural networks are mentioned e.g. genetic algorithms, fuzzy logic, chaos. Just the functionality of these subjects is described; enough to get you started. Lots of references are given to more elaborate descriptions. Easy to read, no extensive mathematical background necessary. 2.) The classics: Kohonen, T. (1984). Self-organization and Associative Memory. Springer-Verlag: New York. (2nd Edition: 1988; 3rd edition: 1989). Comments: "The section on Pattern mathematics is excellent." Rumelhart, D. E. and McClelland, J. L. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition (volumes 1 & 2). The MIT Press. Comments: "As a computer scientist I found the two Rumelhart and McClelland books really heavy going and definitely not the sort of thing to read if you are a beginner."; "It's quite readable, and affordable (about $65 for both volumes)."; "THE Connectionist bible.". 3.) Introductory journal articles: Hinton, G. E. (1989). Connectionist learning procedures. Artificial Intelligence, Vol. 40, pp. 185--234. Comments: "One of the better neural networks overview papers, although the distinction between network topology and learning algorithm is not always very clear. Could very well be used as an introduction to neural networks." Knight, K. (1990). Connectionist, Ideas and Algorithms. Communications of the ACM. November 1990. Vol.33 nr.11, pp 59-74. Comments:"A good article, while it is for most people easy to find a copy of this journal." Kohonen, T. (1988). An Introduction to Neural Computing. Neural Networks, vol. 1, no. 1. pp. 3-16. Comments: "A general review". 4.) Not-quite-so-introductory literature: Anderson, J. A. and Rosenfeld, E. (Eds). (1988). Neurocomputing: Foundations of Research. The MIT Press: Cambridge, MA. Comments: "An expensive book, but excellent for reference. It is a collection of reprints of most of the major papers in the field."; Anderson, J. A., Pellionisz, A. and Rosenfeld, E. (Eds). (1990). Neurocomputing 2: Directions for Research. The MIT Press: Cambridge, MA. Comments: "The sequel to their well-known Neurocomputing book." Caudill, M. and Butler, C. (1990). Naturally Intelligent Systems. MIT Press: Cambridge, Massachusetts. (ISBN 0-262-03156-6). Comments: "I guess one of the best books I read"; "May not be suited for people who want to do some research in the area". Khanna, T. (1990). Foundations of Neural Networks. Addison-Wesley: New York. Comments: "Not so bad (with a page of erroneous formulas (if I remember well), and #hidden layers isn't well described)."; "Khanna's intention in writing his book with math analysis should be commended but he made several mistakes in the math part". Kung, S.Y. (1993). Digital Neural Networks, Prentice Hall, Englewood Cliffs, NJ. Levine, D. S. (1990). Introduction to Neural and Cognitive Modeling. Lawrence Erlbaum: Hillsdale, N.J. Comments: "Highly recommended". Lippmann, R. P. (April 1987). An introduction to computing with neural nets. IEEE Acoustics, Speech, and Signal Processing Magazine. vol. 2, no. 4, pp 4-22. Comments: "Much acclaimed as an overview of neural networks, but rather inaccurate on several points. The categorization into binary and continuous- valued input neural networks is rather arbitrary, and may work confusing for the unexperienced reader. Not all networks discussed are of equal importance." Maren, A., Harston, C. and Pap, R., (1990). Handbook of Neural Computing Applications. Academic Press. ISBN: 0-12-471260-6. (451 pages) Comments: "They cover a broad area"; "Introductory with suggested applications implementation". Pao, Y. H. (1989). Adaptive Pattern Recognition and Neural Networks Addison-Wesley Publishing Company, Inc. (ISBN 0-201-12584-6) Comments: "An excellent book that ties together classical approaches to pattern recognition with Neural Nets. Most other NN books do not even mention conventional approaches." Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, vol 323 (9 October), pp. 533-536. Comments: "Gives a very good potted explanation of backprop NN's. It gives sufficient detail to write your own NN simulation." Simpson, P. K. (1990). Artificial Neural Systems: Foundations, Paradigms, Applications and Implementations. Pergamon Press: New York. Comments: "Contains a very useful 37 page bibliography. A large number of paradigms are presented. On the negative side the book is very shallow. Best used as a complement to other books". Zeidenberg. M. (1990). Neural Networks in Artificial Intelligence. Ellis Horwood, Ltd., Chichester. Comments: "Gives the AI point of view". Zornetzer, S. F., Davis, J. L. and Lau, C. (1990). An Introduction to Neural and Electronic Networks. Academic Press. (ISBN 0-12-781881-2) Comments: "Covers quite a broad range of topics (collection of articles/papers )."; "Provides a primer-like introduction and overview for a broad audience, and employs a strong interdisciplinary emphasis". ------------------------------------------------------------------------ -A11.) Any journals and magazines about Neural Networks ? [to be added: comments on speed of reviewing and publishing, whether they accept TeX format or ASCII by e-mail, etc.] A. Dedicated Neural Network Journals: ===================================== Title: Neural Networks Publish: Pergamon Press Address: Pergamon Journals Inc., Fairview Park, Elmsford, New York 10523, USA and Pergamon Journals Ltd. Headington Hill Hall, Oxford OX3, 0BW, England Freq.: 6 issues/year (vol. 1 in 1988) Cost/Yr: Free with INNS membership ($45?), Individual $65, Institution $175 ISSN #: 0893-6080 Remark: Official Journal of International Neural Network Society (INNS). Contains Original Contributions, Invited Review Articles, Letters to Editor, Invited Book Reviews, Editorials, Announcements and INNS News, Software Surveys. This is probably the most popular NN journal. (Note: Remarks supplied by Mike Plonski "plonski@aero.org") ------- Title: Neural Computation Publish: MIT Press Address: MIT Press Journals, 55 Hayward Street Cambridge, MA 02142-9949, USA, Phone: (617) 253-2889 Freq.: Quarterly (vol. 1 in 1989) Cost/Yr: Individual $45, Institution $90, Students $35; Add $9 Outside USA ISSN #: 0899-7667 Remark: Combination of Reviews (10,000 words), Views (4,000 words) and Letters (2,000 words). I have found this journal to be of outstanding quality. (Note: Remarks supplied by Mike Plonski "plonski@aero.org") ----- Title: IEEE Transaction on Neural Networks Publish: Institute of Electrical and Electronics Engineers (IEEE) Address: IEEE Service Cemter, 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ, 08855-1331 USA. Tel: (201) 981-0060 Cost/Yr: $10 for Members belonging to participating IEEE societies Freq.: Quarterly (vol. 1 in March 1990) Remark: Devoted to the science and technology of neural networks which disclose significant technical knowledge, exploratory developments and applications of neural networks from biology to software to hardware. Emphasis is on artificial neural networks. Specific aspects include self organizing systems, neurobiological connections, network dynamics and architecture, speech recognition, electronic and photonic implementation, robotics and controls. Includes Letters concerning new research results. (Note: Remarks are from journal announcement) ----- Title: International Journal of Neural Systems Publish: World Scientific Publishing Address: USA: World Scientific Publishing Co., 687 Hartwell Street, Teaneck, NJ 07666. Tel: (201) 837-8858; Eurpoe: World Scientific Publishing Co. Pte. Ltd., 73 Lynton Mead, Totteridge, London N20-8DH, England. Tel: (01) 4462461; Other: World Scientific Publishing Co. Pte. Ltd., Farrer Road, P.O. Box 128, Singapore 9128. Tel: 2786188 Freq.: Quarterly (Vol. 1 in 1990?) Cost/Yr: Individual $42, Institution $88 (plus $9-$17 for postage) ISSN #: 0129-0657 (IJNS) Remark: The International Journal of Neural Systems is a quarterly journal which covers information processing in natural and artificial neural systems. It publishes original contributions on all aspects of this broad subject which involves physics, biology, psychology, computer science and engineering. Contributions include research papers, reviews and short communications. The journal presents a fresh undogmatic attitude towards this multidisciplinary field with the aim to be a forum for novel ideas and improved understanding of collective and cooperative phenomena with computational capabilities. (Note: Remarks supplied by B. Lautrup (editor), "LAUTRUP%nbivax.nbi.dk@CUNYVM.CUNY.EDU" ) Review is reported to be very slow. ------ Title: Neural Network News Publish: AIWeek Inc. Address: Neural Network News, 2555 Cumberland Parkway, Suite 299, Atlanta, GA 30339 USA. Tel: (404) 434-2187 Freq.: Monthly (beginning September 1989) Cost/Yr: USA and Canada $249, Elsewhere $299 Remark: Commericial Newsletter ------ Title: Network: Computation in Neural Systems Publish: IOP Publishing Ltd Address: Europe: IOP Publishing Ltd, Techno House, Redcliffe Way, Bristol BS1 6NX, UK; IN USA: American Institute of Physics, Subscriber Services 500 Sunnyside Blvd., Woodbury, NY 11797-2999 Freq.: Quarterly (1st issue 1990) Cost/Yr: USA: $180, Europe: 110 pounds Remark: Description: "a forum for integrating theoretical and experimental findings across relevant interdisciplinary boundaries." Contents: Submitted articles reviewed by two technical referees paper's interdisciplinary format and accessability." Also Viewpoints and Reviews commissioned by the editors, abstracts (with reviews) of articles published in other journals, and book reviews. Comment: While the price discourages me (my comments are based upon a free sample copy), I think that the journal succeeds very well. The highest density of interesting articles I have found in any journal. (Note: Remarks supplied by brandt kehoe "kehoe@csufres.CSUFresno.EDU") ------ Title: Connection Science: Journal of Neural Computing, Artificial Intelligence and Cognitive Research Publish: Carfax Publishing Address: Europe: Carfax Publishing Company, P. O. Box 25, Abingdon, Oxfordshire OX14 3UE, UK. USA: Carafax Publishing Company, 85 Ash Street, Hopkinton, MA 01748 Freq.: Quarterly (vol. 1 in 1989) Cost/Yr: Individual $82, Institution $184, Institution (U.K.) 74 pounds ----- Title: International Journal of Neural Networks Publish: Learned Information Freq.: Quarterly (vol. 1 in 1989) Cost/Yr: 90 pounds ISSN #: 0954-9889 Remark: The journal contains articles, a conference report (at least the issue I have), news and a calendar. (Note: remark provided by J.R.M. Smits "anjos@sci.kun.nl") ----- Title: Concepts in NeuroScience Publish: World Scientific Publishing Address: Same Address (?) as for International Journal of Neural Systems Freq.: Twice per year (vol. 1 in 1989) Remark: Mainly Review Articles(?) (Note: remarks by Osamu Saito "saito@nttica.NTT.JP") ----- Title: International Journal of Neurocomputing Publish: Elsevier Science Publishers Freq.: Quarterly (vol. 1 in 1989) Remark: Review has been reported to be fast (less than 3 months) ----- Title: Neurocomputers Publish: Gallifrey Publishing Address: Gallifrey Publishing, PO Box 155, Vicksburg, Michigan, 49097, USA Tel: (616) 649-3772 Freq. Monthly (1st issue 1987?) ISSN #: 0893-1585 Editor: Derek F. Stubbs Cost/Yr: $32 (USA, Canada), $48 (elsewhere) Remark: I only have one exemplar so I cannot give you much detail about the contents. It is a very small one (12 pages) but it has a lot of (short) information in it about e.g. conferences, books, (new) ideas etc. I don't think it is very expensive but I'm not sure. (Note: remark provided by J.R.M. Smits "anjos@sci.kun.nl") ------ Title: JNNS Newsletter (Newsletter of the Japan Neural Network Society) Publish: The Japan Neural Network Society Freq.: Quarterly (vol. 1 in 1989) Remark: (IN JAPANESE LANGUAGE) Official Newsletter of the Japan Neural Network Society(JNNS) (Note: remarks by Osamu Saito "saito@nttica.NTT.JP") ------- Title: Neural Networks Today Remark: I found this title in a bulletin board of october last year. It was a message of Tim Pattison, timpatt@augean.OZ (Note: remark provided by J.R.M. Smits "anjos@sci.kun.nl") ----- Title: Computer Simulations in Brain Science ----- Title: Internation Journal of Neuroscience ----- Title: Neural Network Computation Remark: Possibly the same as "Neural Computation" ----- Title: Neural Computing and Applications Freq.: Quarterly Publish: Springer Verlag Cost/yr: 120 Pounds Remark: Is the journal of the Neural Computing Applications Forum. Publishes original research and other information in the field of practical applications of neural computing. B. NN Related Journals ====================== Title: Complex Systems Publish: Complex Systems Publications Address: Complex Systems Publications, Inc., P.O. Box 6149, Champaign, IL 61821-8149, USA Freq.: 6 times per year (1st volume is 1987) ISSN #: 0891-2513 Cost/Yr: Individual $75, Institution $225 Remark: Journal COMPLEX SYSTEMS devotes to the rapid publication of research on the science, mathematics, and engineering of systems with simple components but complex overall behavior. Send mail to "jcs@complex.ccsr.uiuc.edu" for additional info. (Remark is from announcement on Net) ----- Title: Biological Cybernetics (Kybernetik) Publish: Springer Verlag Remark: Monthly (vol. 1 in 1961) ----- Title: Various IEEE Transactions and Magazines Publish: IEEE Remark: Primarily see IEEE Trans. on System, Man and Cybernetics; Various Special Issues: April 1990 IEEE Control Systems Magazine.; May 1989 IEEE Trans. Circuits and Systems.; July 1988 IEEE Trans. Acoust. Speech Signal Process. ----- Title: The Journal of Experimental and Theoretical Artificial Intelligence Publish: Taylor & Francis, Ltd. Address: London, New York, Philadelphia Freq.: ? (1st issue Jan 1989) Remark: For submission information, please contact either of the editors: Eric Dietrich Chris Fields PACSS - Department of Philosophy Box 30001/3CRL SUNY Binghamton New Mexico State University Binghamton, NY 13901 Las Cruces, NM 88003-0001 dietrich@bingvaxu.cc.binghamton.edu cfields@nmsu.edu ----- Title: The Behavioral and Brain Sciences Publish: Cambridge University Press Remark: (Expensive as hell, I'm sure.) This is a delightful journal that encourages discussion on a variety of controversial topics. I have especially enjoyed reading some papers in there by Dana Ballard and Stephen Grossberg (separate papers, not collaborations) a few years back. They have a really neat concept: they get a paper, then invite a number of noted scientists in the field to praise it or trash it. They print these commentaries, and give the author(s) a chance to make a rebuttal or concurrence. Sometimes, as I'm sure you can imagine, things get pretty lively. I'm reasonably sure they are still at it--I think I saw them make a call for reviewers a few months ago. Their reviewers are called something like Behavioral and Brain Associates, and I believe they have to be nominated by current associates, and should be fairly well established in the field. That's probably more than I really know about it but maybe if you post it someone who knows more about it will correct any errors I have made. The main thing is that I liked the articles I read. (Note: remarks by Don Wunsch ) ----- Title: International Journal of Applied Intelligence Publish: Kluwer Academic Publishers Remark: first issue in 1990(?) ----- Title: Bulletin of Mathematica Biology ----- Title: Intelligence ----- Title: Journal of Mathematical Biology ----- Title: Journal of Complex System ----- Title: AI Expert Publish: Miller Freeman Publishing Co., for subscription call ++415-267-7672. Remark: Regularly includes ANN related articles, product announcements, and application reports. Listings of ANN programs are available on AI Expert affiliated BBS's ----- Title: International Journal of Modern Physics C Publish: World Scientific Publ. Co. Farrer Rd. P.O.Box 128, Singapore 9128 or: 687 Hartwell St., Teaneck, N.J. 07666 U.S.A or: 73 Lynton Mead, Totteridge, London N20 8DH, England Freq: published quarterly Eds: G. Fox, H. Herrmann and K. Kaneko ----- Title: Machine Learning Publish: Kluwer Academic Publishers Address: Kluwer Academic Publishers P.O. Box 358 Accord Station Hingham, MA 02018-0358 USA Freq.: Monthly (8 issues per year; increasing to 12 in 1993) Cost/Yr: Individual $140 (1992); Member of AAAI or CSCSI $88 Remark: Description: Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive research results on a wide range of learning methods applied to a variety of task domains. The ideal paper will make a theoretical contribution supported by a computer implementation. The journal has published many key papers in learning theory, reinforcement learning, and decision tree methods. Recently it has published a special issue on connectionist approaches to symbolic reasoning. The journal regularly publishes issues devoted to genetic algorithms as well. ----- Title: Journal of Physics A: Mathematical and General Publish: Inst. of Physics, Bristol Freq: 24 issues per year. Remark: Statistical mechanics aspects of neural networks (mostly Hopfield models). ----- Title: Physical Review A: Atomic, Molecular and Optical Physics Publish: The American Physical Society (Am. Inst. of Physics) Freq: Monthly Remark: Statistical mechanics of neural networks. C. Journals loosely related to NNs ================================== JOURNAL OF COMPLEXITY (Must rank alongside Wolfram's Complex Systems) IEEE ASSP Magazine (April 1987 had the Lippmann intro. which everyone likes to cite) ARTIFICIAL INTELLIGENCE (Vol 40, September 1989 had the survey paper by Hinton) COGNITIVE SCIENCE (the Boltzmann machine paper by Ackley et al appeared here in Vol 9, 1983) COGNITION (Vol 28, March 1988 contained the Fodor and Pylyshyn critique of connectionism) COGNITIVE PSYCHOLOGY (no comment!) JOURNAL OF MATHEMATICAL PSYCHOLOGY (several good book reviews) ------------------------------------------------------------------------ -A12.) The most important conferences concerned with Neural Networks ? [to be added: has taken place how often yet; most emphasized topics; where to get proceedings/calls-for-papers etc. ] A. Dedicated Neural Network Conferences: 1. Neural Information Processing Systems (NIPS) Annually since 1988 in Denver, Colorado; late November or early December; (Interdisciplinary conference with computer science, physics, engineering, biology, medicine, cognitive science topics. Covers all aspects of NNs) 2. International Joint Conference on Neural Networks (IJCNN) co-sponsored by INNS and IEEE 3. Annual Conference on Neural Networks (ACNN) 4. International Conference on Artificial Neural Networks (ICANN) Annually in Europe. First was 1991. Major conference of European Neur. Netw. Soc. (ENNS) 5. Artificial Neural Networks in Engineering (ANNIE) Anually since 1991 in St. Louis, Missouri; held in November. (Topics: NN architectures, pattern recognition, neuro-control, neuro-engineering systems. Contact: ANNIE; Engineering Management Department; 223 Engineering Management Building; University of Missouri-Rolla; Rolla, MO 65401; FAX: (314) 341-6567) B. Other Conferences 1. International Joint Conference on Artificial Intelligence (IJCAI) 2. Intern. Conf. on Acustics, Speech and Signal Processing (ICASSP) 3. Annual Conference of the Cognitive Science Society 4. [Vision Conferences?] C. Pointers to Conferences 1. The journal "Neural Networks" has a long list of conferences, workshops and meetings in each issue. This is quite interdisciplinary. 2. There is a regular posting on comp.ai.neural-nets from Paultje Bakker: "Upcoming Neural Network Conferences", which lists names, dates, locations, contacts, and deadlines. ------------------------------------------------------------------------ -A13.) Neural Network Associations ? [Is this data still correct ? Who will send me some update ?] 1. International Neural Network Society (INNS). INNS membership includes subscription to "Neural Networks", the official journal of the society. Membership is $55 for non-students and $45 for students per year. Address: INNS Membership, P.O. Box 491166, Ft. Washington, MD 20749. 2. International Student Society for Neural Networks (ISSNNets). Membership is $5 per year. Address: ISSNNet, Inc., P.O. Box 15661, Boston, MA 02215 USA 3. Women In Neural Network Research and technology (WINNERS). Address: WINNERS, c/o Judith Dayhoff, 11141 Georgia Ave., Suite 206, Wheaton, MD 20902. Telephone: 301-933-9000. 4. European Neural Network Society (ENNS) 5. Japanese Neural Network Society (JNNS) Address: Japanese Neural Network Society Department of Engineering, Tamagawa University, 6-1-1, Tamagawa Gakuen, Machida City, Tokyo, 194 JAPAN Phone: +81 427 28 3457, Fax: +81 427 28 3597 6. Association des Connexionnistes en THese (ACTH) (the French Student Association for Neural Networks) Membership is 100 FF per year Activities : newsletter, conference (every year), list of members... Address : ACTH - Le Castelnau R2 23 avenue de la Galline 34170 Castelnau-le-Lez FRANCE Contact : jdmuller@vnet.ibm.com 7. Neurosciences et Sciences de l'Ingenieur (NSI) Biology & Computer Science Activity : conference (every year) Address : NSI - TIRF / INPG 46 avenue Felix Viallet 38031 Grenoble Cedex FRANCE ------------------------------------------------------------------------ -A14.) Other sources of information about NNs ? 1. Neuron Digest Internet Mailing List. From the welcome blurb: "Neuron-Digest is a list (in digest form) dealing with all aspects of neural networks (and any type of network or neuromorphic system)" Moderated by Peter Marvit. To subscribe, send email to neuron-request@cattell.psych.upenn.edu comp.ai.neural-net readers also find the messages in that newsgroup in the form of digests. 2. Usenet groups comp.ai.neural-nets (Oha ! :-> ) and comp.theory.self-org-sys There is a periodic posting on comp.ai.neural-nets sent by srctran@world.std.com (Gregory Aharonian) about Neural Network patents. 3. Central Neural System Electronic Bulletin Board Modem: 409-589-3338; Sysop: Wesley R. Elsberry; P.O. Box 4201, College Station, TX 77843; welsberr@orca.tamu.edu Many MS-DOS PD and shareware simulations, source code, benchmarks, demonstration packages, information files; some Unix, Macintosh, Amiga related files. Also available are files on AI, AI Expert listings 1986-1991, fuzzy logic, genetic algorithms, artificial life, evolutionary biology, and many Project Gutenberg and Wiretap etexts. No user fees have ever been charged. Home of the NEURAL_NET Echo, available thrugh FidoNet, RBBS-Net, and other EchoMail compatible bulletin board systems. 4. Neural ftp archive site ftp.funet.fi Is administrating a large collection of neural network papers and software at the Finnish University Network file archive site ftp.funet.fi in directory /pub/sci/neural Contains all the public domain software and papers that they have been able to find. All of these files have been transferred from FTP sites in U.S. and are mirrored about every 3 months at fastest. Contact: neural-adm@ftp.funet.fi 5. USENET newsgroup comp.org.issnnet Forum for discussion of academic/student-related issues in NNs, as well as information on ISSNNet (see A13) and its activities. 6. AI CD-ROM Network Cybernetics Corporation produces the "AI CD-ROM". It is an ISO-9660 format CD-ROM and contains a large assortment of software related to artificial intelligence, artificial life, virtual reality, and other topics. Programs for OS/2, MS-DOS, Macintosh, UNIX, and other operating systems are included. Research papers, tutorials, and other text files are included in ASCII, RTF, and other universal formats. The files have been collected from AI bulletin boards, Internet archive sites, University computer deptartments, and other government and civilian AI research organizations. Network Cybernetics Corporation intends to release annual revisions to the AI CD-ROM to keep it up to date with current developments in the field. The AI CD-ROM includes collections of files that address many specific AI/AL topics including: [... some stuff deleted...] - Neural Networks: Source code and executables for many different platforms including Unix, DOS, and Macintosh. ANN development tools, example networks, sample data, and tutorials are included. A complete collection of Neural Digest is included as well. [... lots of stuff deleted...] The AI CD-ROM may be ordered directly by check, money order, bank draft, or credit card from: Network Cybernetics Corporation 4201 Wingren Road Suite 202 Irving, TX 75062-2763 Tel 214/650-2002 Fax 214/650-1929 The cost is $129 per disc + shipping ($5/disc domestic or $10/disc foreign) (See the comp.ai FAQ for further details) 7. http://www.eeb.ele.tue.nl In World-Wide-Web (WWW, for example via the xmosaic program) you can read neural network information by opening the universal resource locator (URL) http://www.eeb.ele.tue.nl It contains a hypertext version of this FAQ and other NN-related information. 8. Neurosciences Internet Resource Guide This document aims to be a guide to existing, free, Internet-accessible resources helpful to neuroscientists of all stripes. An **ASCII text version** (86K) is available in the Clearinghouse of Subject-Oriented Internet Resource Guides as follows: anonymous FTP: host: una.hh.lib.umich.edu path: /inetdirsstacks file: neurosci:cormbonario gopher: via U. Minnesota list of gophers menu: North America/USA/Michigan/Clearinghouse.../ All Guides/Neurosciences WWW: gopher://una.hh.lib.umich.edu/00/inetdirsstacks/ neurosci:cormbonario We are also creating a **hypertext version** of the guide: WWW: http://http2.sils.umich.edu/Public/nirg/nirg1.html ------------------------------------------------------------------------ -A15.) Freely available software packages for NN simulation ? [This is a bit chaotic and needs reorganization. A bit more information about what the various programs can do, on which platform they run, and how big they are would also be nice. And some important packages are still missing (?) Who volunteers for that ?] 1. Rochester Connectionist Simulator A quite versatile simulator program for arbitrary types of neural nets. Comes with a backprop package and a X11/Sunview interface. anonymous FTP from cs.rochester.edu (192.5.53.209) directory : pub/simulator files: README (8 KB) (documentation:) rcs_v4.2.justdoc.tar.Z (1.6 MB) (source code:) rcs_v4.2.justsrc.tar.Z (1.4 MB) 2. UCLA-SFINX ftp 131.179.16.6 (retina.cs.ucla.edu) Name: sfinxftp Password: joshua directory: pub/ files : README sfinx_v2.0.tar.Z Email info request : sfinx@retina.cs.ucla.edu 3. NeurDS request from mcclanahan%cookie.dec.com@decwrl.dec.com simulator for DEC systems supporting VT100 terminal. OR anonymous ftp gatekeeper.dec.com [16.1.0.2] directory: pub/DEC file: NeurDS031.tar.Z ( please check may be NeurDSO31.tar.Z ) 4. PlaNet5.7 (also known as SunNet) ftp 133.15.240.3 (tutserver.tut.ac.jp) pub/misc/PlaNet5.7.tar.Z or ftp 128.138.240.1 (boulder.colorado.edu) pub/generic-sources/PlaNet5.7.tar.Z (also the old PlaNet5.6.tar.Z) A popular connectionist simulator with versions to run under X Windows, and non-graphics terminals created by Yoshiro Miyata (Chukyo Univ., Japan). 60-page User's Guide in Postscript. Send any questions to miyata@sccs.chukyo-u.ac.jp 5. GENESIS GENESIS 1.4.1 (GEneral NEural SImulation System) is a general purpose simulation platform which was developed to support the simulation of neural systems ranging from complex models of single neurons to simulations of large networks made up of more abstract neuronal components. Most current GENESIS applications involve realistic simulations of biological neural systems. Although the software can also model more abstract networks, other simulators are more suitable for backpropagation and similar connectionist modeling. May be obtained via FTP from genesis.cns.caltech.edu [131.215.137.64]. Use 'telnet' to genesis.cns.caltech.edu beforehands and login as the user "genesis" (no password required). If you answer all the questions asked of you an 'ftp' account will automatically be created for you. You can then 'ftp' back to the machine and download the software (ca. 3 MB). Contact: genesis@cns.caltech.edu. 6. Mactivation anonymous ftp from bruno.cs.colorado.edu [128.138.243.151] directory: /pub/cs/misc file: Mactivation-3.3.sea.hqx 7. 8. Cascade Correlation Simulator A simulator based on Scott Fahlman's Cascade Correlation algorithm. Anonymous ftp from ftp.cs.cmu.edu [128.2.206.173] directory /afs/cs/project/connect/code file cascor1a.shar (206 KB) There is also a version of recurrent cascade correlation in the same directory in file rcc1.c (107 KB). 9. Quickprop A variation of the back-propagation algorithm developed by Scott Fahlman. A simulator is available in the same directory as the cascade correlation simulator above in file nevprop116.shar (137 KB) (see also the description of NEVPROP below) 10. DartNet DartNet is a Macintosh-based backpropagation simulator, developed at Dartmouth by Jamshed Bharucha and Sean Nolan as a pedagogical tool. It makes use of the Mac's graphical interface, and provides a number of tools for building, editing, training, testing and examining networks. This program is available by anonymous ftp from dartvax.dartmouth.edu [129.170.16.4] as /pub/mac/dartnet.sit.hqx (124 KB). 11. SNNS "Stuttgart Neural Network Simulator" from the University of Stuttgart, Germany. A luxurious simulator for many types of nets; with X11 interface: Graphical 2D and 3D topology editor/visualizer, training visualisation, etc. Currently supports backpropagation (vanilla, online, with momentum term and flat spot elimination, batch, time delay), counterpropagation, quickprop, backpercolation 1, generalized radial basis functions (RBF), RProp, ART1, ART2, ARTMAP, Cascade Correlation, Recurrent Cascade Correlation, Dynamic LVQ, Backpropagation through time (for recurrent networks), batch backpropagation through time (for recurrent networks), Quickpropagation through time (for recurrent networks), and is user-extendable. ftp: ftp.informatik.uni-stuttgart.de [129.69.211.2] directory /pub/SNNS file SNNSv3.0.tar.Z OR SNNSv3.0.tar.Za[a-d] ( 826 KB) manual SNNSv2.1.Manual.ps.Z (1270 KB) SNNSv2.1.Readme (8052 Bytes) 12. Aspirin/MIGRAINES Aspirin/MIGRAINES 6.0 consists of a code generator that builds neural network simulations by reading a network description (written in a language called "Aspirin") and generates a C simulation. An interface (called "MIGRAINES") is provided to export data from the neural network to visualization tools. The system has been ported to a large number of platforms. The goal of Aspirin is to provide a common extendible front-end language and parser for different network paradigms. The MIGRAINES interface is a terminal based interface that allows you to open Unix pipes to data in the neural network. This replaces the NeWS1.1 graphical interface in version 4.0 of the Aspirin/MIGRAINES software. The new interface is not a simple to use as the version 4.0 interface but is much more portable and flexible. The MIGRAINES interface allows users to output neural network weight and node vectors to disk or to other Unix processes. Users can display the data using either public or commercial graphics/analysis tools. Example filters are included that convert data exported through MIGRAINES to formats readable by Gnuplot 3.0, Matlab, Mathematica, and xgobi. The software is available from two FTP sites: CMU's simulator collection on "pt.cs.cmu.edu" (128.2.254.155) in /afs/cs/project/connect/code/am6.tar.Z". and UCLA's cognitive science machine "ftp.cognet.ucla.edu" (128.97.50.19) in alexis/am6.tar.Z The compressed tar file is a little less than 2 megabytes. 13. Adaptive Logic Network kit Available from menaik.cs.ualberta.ca. This package differs from the traditional nets in that it uses logic functions rather than floating point; for many tasks, ALN's can show many orders of magnitude gain in training and performance speed. Anonymous ftp from menaik.cs.ualberta.ca [129.128.4.241] README /pub/atree/atree.readme (7 KB) unix source code and examples: /pub/atree/atree2.tar.Z (145 KB) Postscript documentation: /pub/atree/atree2.ps.Z ( 76 KB) MS-Windows 3.x executable: /pub/atree/a27exe.exe (412 KB) MS-Windows 3.x source code: /pub/atree/atre27.exe (572 KB) 14. NeuralShell Available from FTP site quanta.eng.ohio-state.edu (128.146.35.1) in directory "pub/NeuralShell", filename "NeuralShell.tar". 15. PDP The PDP simulator package is available via anonymous FTP at nic.funet.fi (128.214.6.100) in /pub/sci/neural/sims/pdp.tar.Z (0.2 MB) The simulator is also available with the book "Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises" by McClelland and Rumelhart. MIT Press, 1988. Comment: "This book is often referred to as PDP vol III which is a very misleading practice! The book comes with software on an IBM disk but includes a makefile for compiling on UNIX systems. The version of PDP available at nic.funet.fi seems identical to the one with the book except for a bug in bp.c which occurs when you try to run a script of PDP commands using the DO command. This can be found and fixed easily." 16. Xerion Xerion is available via anonymous ftp from ftp.cs.toronto.edu in the directory /pub/xerion. xerion-3.1.ps.Z (153 kB) and xerion-3.1.tar.Z (1322 kB) plus several concrete simulators built with xerion (about 40 kB each). Xerion runs on SGI and Sun machines and uses X Windows for graphics. The software contains modules that implement Back Propagation, Recurrent Back Propagation, Boltzmann Machine, Mean Field Theory, Free Energy Manipulation, Hard and Soft Competitive Learning, and Kohonen Networks. Sample networks built for each of the modules are also included. Contact: xerion@ai.toronto.edu 17. Neocognitron simulator An implementation is available for anonymous ftp at [128.194.15.32] tamsun.tamu.edu as /pub/neocognitron.Z.tar or [129.12.21.7] unix.hensa.ac.uk as /pub/uunet/pub/ai/neural/neocognitron.tar.Z The simulator is written in C and comes with a list of references which are necessary to read to understand the specifics of the implementation. The unsupervised version is coded without (!) C-cell inhibition. 18. Multi-Module Neural Computing Environment (MUME) MUME is a simulation environment for multi-modules neural computing. It provides an object oriented facility for the simulation and training of multiple nets with various architectures and learning algorithms. MUME includes a library of network architectures including feedforward, simple recurrent, and continuously running recurrent neural networks. Each architecture is supported by a variety of learning algorithms. MUME can be used for large scale neural network simulations as it provides support for learning in multi-net environments. It also provide pre- and post-processing facilities. The modules are provided in a library. Several "front-ends" or clients are also available. X-Window support by editor/visualization tool Xmume. MUME can be used to include non-neural computing modules (decision trees, ...) in applications. Version 0.73 of MUME has been deposited for anonymous ftp on mickey.sedal.su.oz.au (129.78.24.170) in directory /mume. Contact: Marwan Jabri, SEDAL, Sydney University Electrical Engineering, NSW 2006 Australia, marwan@sedal.su.oz.au 19. LVQ_PAK, SOM_PAK These are packages for Learning Vector Quantization and Self-Organizing Maps, respectively. They have been built by the LVQ/SOM Programming Team of the Helsinki University of Technology, Laboratory of Computer and Information Science, Rakentajanaukio 2 C, SF-02150 Espoo, FINLAND There are versions for Unix and MS-DOS available from cochlea.hut.fi (130.233.168.48) in /pub/lvq_pak/lvq_pak-2.1.tar.Z (340 kB, Unix) /pub/lvq_pak/lvq_p2r1.exe (310 kB, MS-DOS self-extract archive) /pub/som_pak/som_pak-1.1.tar.Z (246 kB, Unix) /pub/som_pak/som_p1r1.exe (215 kB, MS-DOS self-extract archive) 20. SESAME (Software Environment for the Simulation of Adaptive Modular Systems) SESAME is a prototypical software implementation which facilitates * Object-oriented building blocks approach. * Contains a large set of C++ classes useful for neural nets, neurocontrol and pattern recognition. No C++ classes can be used as stand alone, though! * C++ classes include CartPole, nondynamic two-robot arms, Lunar Lander, Backpropagation, Feature Maps, Radial Basis Functions, TimeWindows, Fuzzy Set Coding, Potential Fields, Pandemonium, and diverse utility building blocks. * A kernel which is the framework for the C++ classes and allows run-time manipulation, construction, and integration of arbitrary complex and hybrid experiments. * Currently no graphic interface for construction, only for visualization. * Platform is SUN4, XWindows Unfortunately no reasonable good introduction has been written until now. We hope to have something soon. For now we provide papers (eg. NIPS-92), a reference manual (>220 pages), source code (ca. 35.000 lines of code), and a SUN4-executable by ftp only. Sesame and its description is available for anonymous ftp on ftp ftp.gmd.de [129.26.8.90] in the directories gmd/as/sesame and gmd/as/paper Questions please to sesame-request@gmd.de There is only very limited support available. Currently we can not handle many users. 21. Nevada Backpropagation (NevProp) NevProp is a free, easy-to-use feedforward backpropagation (multilayer perceptron) program. It uses an interactive character-based interface, and is distributed as C source code that should compile and run on most platforms. (Precompiled executables are available for Macintosh and DOS.) The original version was Quickprop 1.0 by Scott Fahlman, as translated from Common Lisp by Terry Regier. We added early-stopped training based on a held-out subset of data, c index (ROC curve area) calculation, the ability to force gradient descent (per-epoch or per-pattern), and additional options. *** FEATURES: NevProp version 1.16... o UNLIMITED (except by machine memory) number of input PATTERNS; o UNLIMITED number of input, hidden, and output UNITS; o Arbitrary CONNECTIONS among the various layers' units; o Clock-time or user-specified RANDOM SEED for initial random weights; o Choice of regular GRADIENT DESCENT or QUICKPROP; o Choice of PER-EPOCH or PER-PATTERN (stochastic) weight updating; o GENERALIZATION to a test dataset; o AUTOMATICALLY STOPPED TRAINING based on generalization; o RETENTION of best-generalizing weights and predictions; o Simple but useful GRAPHIC display to show smoothness of generalization; o SAVING of results to a file while working interactively; o SAVING of weights file and reloading for continued training; o PREDICTION-only on datasets by applying an existing weights file; o In addition to RMS error, the concordance, or c index is displayed. The c index (area under the ROC curve) shows the correctness of the RELATIVE ordering of predictions AMONG the cases; ie, it is a measure of discriminative power of the model. *** AVAILABILITY: The most updated version of NevProp will be made available by anonymous ftp from the University of Nevada, Reno: "ftp.scs.unr.edu" in the directory "pub/goodman/nevpropdir". *** PLANS FOR NEXT RELEASE: Version 2 to be released in Spring of 1994 -- some of the new features: more flexible file formatting (including access to external data files; option to prerandomize data order; randomized stochastic gradient descent; option to rescale predictor (input) variables); linear output units as an alternative to sigmoidal units for use with continuous-valued dependent variables (output targets); cross-entropy (maximum likelihood) criterion function as an alternative to square error for use with categorical dependent variables (classification/symbolic/nominal targets); and interactive interrupt to change settings on-the-fly. (If you'd like to beta test prerelease version, contact goodman@unr.edu) *** SUPPORT: Limited support is available from Phil Goodman (goodman@unr.edu), University of Nevada Center for Biomedical Research. 22. Fuzzy ARTmap Available for anonymous ftp from park.bu.edu [128.176.121.56] as /pub/fuzzy-artmap.tar.Z (44 kB) (This is just a small example program.) 23. PYGMALION This is a prototype that stems from an ESPRIT project. It implements back-propagation, self organising map, and Hopfield nets. On imag.imag.fr [129.88.32.1] in directory archive/pygmalion: pygmalion.tar.Z (1534 kb) 24. Basis-of-AI-backprop: Here are some of the details of a set of back-propagation programs I have been working on. Earlier versions have been posted in comp.sources.misc and people around the world have used them and liked them. This package is free for ordinary users but shareware for businesses and government agencies ($200/copy, but then for this you get the professional version as well). I do support this package via email. Some of the highlights are: * in C for UNIX and DOS and DOS binaries * gradient descent, delta-bar-delta and quickprop * extra fast 16-bit fixed point weight version as well as a conventional floating point version * recurrent networks * numerous sample problems To get this version simply ftp to ftp.mcs.com where you will land in the directory /work/public/mcsnet.users. Then cd to drt and read readme.1st. The expanded professional version is $30/copy for ordinary individuals including academics and $200/copy for businesses and government agencies. Prices and contents subject to change without notice. Some of the highlights are an improved user interface, more activation functions, networks can be read into your own programs, dynamic node creation, weight decay, SuperSAB Contact: Don Tveter; 5228 N. Nashville Ave.; Chicago, Illinois 60656 drt@mcs.com 25. Matrix Backpropagation MBP (Matrix Back Propagation) is an efficient implementation of the back-propagation algorithm for current-generation workstations. The algorithm includes a per-epoch adaptive technique for gradient descent. All the computations are done through matrix multiplications and make use of highly optimized C code. The goal is to reach almost peak-performances on RISCs with superscalar capabilities and fast caches. On some machines (and with large networks) a 30-40x speed-up can be measured respect to conventional implementations. The software is available by anonymous ftp from risc6000.dibe.unige.it:/pub/ [130.251.89.154] as MBPv1.1.tar.Z (unix version) and MBPv11.zip (DOS version). The documentation is included in the distribution as the postscript file mbpv11.ps. For more information, contact Davide Anguita or . 26. WinNN WinNN is a shareware Neural Networks (NN) package for windows 3.1. WinNN incorporates a very user friendly interface with a powerful computational engine. WinNN is intended to be used as a tool for beginners and more advanced neural networks users, it provides an alternative to using more expensive and hard to use packages. WinNN can implement feed forward multi-layered NN and uses a modified fast back-propagation for training. Extensive on line help. Has various neuron functions. Allows on the fly testing of the network performance and generalization. All training parameters can be easily modified while WinNN is training. Results can be saved on disk or copied to the clipboard. Supports plotting of the outputs and weight distribution. Available for ftp from wuarcive.wustl.edu in pub/MSDOS_UPLOADS/win3 and pub/MSDOS_UPLOADS/win; the file name is WINNN09.ZIP (542 kB). For some of these simulators there are user mailing lists. Get the packages and look into their documentation for further info. If you are using a small computer (PC, Mac, etc.) you may want to have a look at the Central Neural System Electronic Bulletin Board (see Answer 14) Modem: 509-627-6CNS; Sysop: Wesley R. Elsberry; P.O. Box 1187, Richland, WA 99352; welsberr@sandbox.kenn.wa.us There are lots of small simulator packages, the CNS ANNSIM file set. There is an ftp mirror site for the CNS ANNSIM file set at me.uta.edu (129.107.2.20) in the /pub/neural directory. Most ANN offerings are in /pub/neural/annsim. ------------------------------------------------------------------------ -A16.) Commercial software packages for NN simulation ? [preliminary] [who will write some short comment on each of the most important packages ?] The Number 1 of each volume of the journal "Neural Networks" has a list of some dozens of commercial suppliers of Neural Network things: Software, Hardware, Support, Programming, Design and Service. 1. nn/xnn Name: nn/xnn Company: Neureka ANS Address: Klaus Hansens vei 31B 5037 Solheimsviken NORWAY Phone: +47-55544163 / +47-55201548 Email: arnemo@eik.ii.uib.no Basic capabilities: Neural network development tool. nn is a language for specification of neural network simulators. Produces C-code and executables for the specified models, therefore ideal for application development. xnn is a graphical front-end to nn and the simulation code produced by nn. Gives graphical representations in a number of formats of any variables during simulation run-time. Comes with a number of pre-implemented models, including: Backprop (several variants), Self Organizing Maps, LVQ1, LVQ2, Radial Basis Function Networks, Generalized Regression Neural Networks, Jordan nets, Elman nets, Hopfield, etc. Operating system: nn: UNIX or MS-DOS, xnn: UNIX/X-windows System requirements: 10 Mb HD, 2 Mb RAM Approx. price: USD 2000,- 2. BrainMaker Name: BrainMaker, BrainMaker Pro Company: California Scientific Software Address: 10024 Newtown rd, Nevada City, CA, 95959 USA Phone,Fax: 916 478 9040, 916 478 9041 Email: calsci!mittmann@gvgpsa.gvg.tek.com (flakey connection) Basic capabilities: train backprop neural nets Operating system: DOS, Windows, Mac System requirements: Uses XMS or EMS for large models(PCs only): Pro version Approx. price: $195, $795 BrainMaker Pro 3.0 (DOS/Windows) $795 Gennetic Training add-on $250 ainMaker 3.0 (DOS/Windows/Mac) $195 Network Toolkit add-on $150 BrainMaker 2.5 Student version (quantity sales only, about $38 each) BrainMaker Pro C30 Accelerator Board w/ 5Mb memory $9750 w/32Mb memory $13,000 Intel iNNTS NN Development System $11,800 Intel EMB Multi-Chip Board $9750 Intel 80170 chip set $940 Introduction To Neural Networks book $30 California Scientific Software can be reached at: Phone: 916 478 9040 Fax: 916 478 9041 Tech Support: 916 478 9035 Mail: 10024 newtown rd, Nevada City, CA, 95959, USA All Software has a 30 day money back guarantee, and unlimited free technical support. BrainMaker package includes: The book Introduction to Neural Networks BrainMaker Users Guide and reference manual 300 pages , fully indexed, with tutorials, and sample Neural Networks Netmaker Netmaker makes building and training Neural Networks easy, by importing and automatically creating BrainMaker's Neural Network files. Netmaker imports Lotus, Excel, dBase, and ASCII files. BrainMaker Full menu and dialog box interface, runs Backprop at 750,000 cps on a 33Mhz 486. Feature BrainMaker Professional Benefit User Interface Pull-down Menus, Dialog Boxes { { easy to learn and use; all parameters saved in a file you can edit. Programmable Output Files { { exports data in your format to spreadsheets, graphics packages, etc. Editing in BrainMaker { { quickly edit data, display, network connections, and more. Network Progress Display { monitors training with a simple graphic display. Fact Annotation { { attaches your comments to examples for display and printing. Printer Support { { HP LaserJet, DeskJet, InkJet, IBM Proprinter, Epson, etc. NetPlotter T { see how the input correlates with your output. Graphics Built In { shows trends, cycles, network responses, statistics, etc.; see it on screen, plotter, or printer. Dynamic Data Exchange { puts your network in other windows programs Performance Binary Mode T { uses binary files for greater speed. Batch Mode { add networks to your existing programs, train while you're away. EMS and XMS Memory { up to 8192 independent variables. Save Network Periodically { { saves results to a file in case of power failure. Fastest Algorithms { { 750,000 connections-per-second (486/50). Neurons per Layer 512 32,000 more inputs: model complex data with ease. Number of Layers 8 8 extra hidden layers can help tackle bigger problems. Training Specify Parameters by Layer { fine-tunes performance inside the netw Recurrence Networks { Puts feedback in your network, automates historical input. Prune Connections and Neurons { improves accuracy by trimming away excess "fat". Add Hidden Neurons In Training { { finds best size network quickly; fully automated with Professional. Custom Neuron Functions { { optimizes training to suit any need. Testing While Training { { trains for best performance on new data. Stop training when... { lets you decide when network has learned well. Heavy Weights { helps networks train. Hypersonic Training T { trains faster with this proprietary algorithm. Analysis, Advanced Functions Sensitivity Analysis { shows you which inputs determined your results. Neuron Sensitivity { shows you the total effect of one input on your results. Global Network Analysis { shows how the networks reacts to your inputs overall Contour Analysis { shows peaks and valleys of the output when two inputs change Data Correlator { finds important data and optimum time delays. Error Statistics Report { { check your network error rate during training. Print or Edit Weight Matrices { { examine, customize network internals. Competitor { ranks horses, teams, stocks, etc. in finish order. Run Time System { C source code - make programs with your network for resale. Chip Support { { Intel, American Neurologics, Micro Devices. Genetic Training Option G trains variations of your design and shows you which was the best. Network Data Management Functions NetMaker { { spreadsheet-like data manipulation and network file creation. NetChecker { { checks your files for errors and inconsistencies. Shuffle { { mixes up the order of examples for better training. Binary T { converts files to binary for quicker training. MinMax { { finds min / max / standard deviation of data for fine-tuned results. Data Importation { { reads data from Lotus, dBASE, Excel, ASCII, binary. Finacial Data { reads MetaStock, and Computrack Data Manipulation { { finds indicators, oscillators, running averages, etc. Cyclic Analysis { checks data for periodic or cyclic behavior. Data Types { { uses symbolic, text, picture, and numeric data. Documentation & User Support User's Guide { { an application development guide and quick-start booklet. Introduction to Neural Networks { { 324 pp, gets you up to date in this exciting field. 3. SAS Software/ Neural Net add-on Name: SAS Software Company: SAS Institute, Inc. Address: SAS Campus Drive, Cary, NC 27513, USA Phone,Fax: (919) 677-8000 Email: saswss@unx.sas.com (Neural net inquiries only) Basic capabilities: Feedforward nets with numerous training methods and loss functions, plus statistical analogs of counterpropagation and various unsupervised architectures Operating system: Lots System requirements: Lots Uses XMS or EMS for large models(PCs only): Runs under Windows, OS/2 Approx. price: Free neural net software, but you have to license SAS/Base software and preferably the SAS/OR, SAS/ETS, and/or SAS/STAT products. Comments: Oriented toward data analysis and statistical applications 4. NeuralWorks Name: NeuralWorks Professional II Plus (from NeuralWare) Company: NeuralWare Inc. Adress: Pittsburgh, PA 15276-9910 Phone: (412) 787-8222 FAX: (412) 787-8220 Distributor for Europe: Scientific Computers GmbH. Franzstr. 107, 52064 Aachen Germany Tel. (49) +241-26041 Fax. (49) +241-44983 Email. info@scientific.de Basic capabilities: supports over 30 different nets: backprop, art-1,kohonen, modular neural network, General regression, Fuzzy art-map, probabilistic nets, self-organizing map, lvq, boltmann, bsb, spr, etc... Extendable with optional package. ExplainNet, Flashcode (compiles net in .c code for runtime), user-defined io in c possible. ExplainNet (to eliminate extra inputs), pruning, savebest,graph.instruments like correlation, hinton diagrams, rms error graphs etc.. Operating system : PC,Sun,IBM RS6000,Apple Macintosh,SGI,Dec,HP. System requirements: varies. PC:2MB extended memory+6MB Harddisk space. Uses windows compatible memory driver (extended). Uses extended memory. Approx. price : call (depends on platform) Comments : award winning documentation, one of the market leaders in NN software. 5. MATLAB Neural Network Toolbox (for use with Matlab 4.x) Contact: The MathWorks, Inc. Phone: 508-653-1415 24 Prime Park Way FAX: 508-653-2997 Natick, MA 01760 email: info@mathworks.com (Comment by Richard Andrew Miles Outerbridge, RAMO@UVPHYS.PHYS.UVIC.CA:) Matlab is spreading like hotcakes (and the educational discounts are very impressive). The newest release of Matlab (4.0) ansrwers the question "if you could only program in one language what would it be?". The neural network toolkit is worth getting for the manual alone. Matlab is available with lots of other toolkits (signal processing, optimization, etc.) but I don't use them much - the main package is more than enough. The nice thing about the Matlab approach is that you can easily interface the neural network stuff with anything else you are doing. 6. Propagator Contact: ARD Corporation, 9151 Rumsey Road, Columbia, MD 21045, USA propagator@ard.com Easy to use neural network training package. A true GUI implementation of backpropagation networks with five layers (32,000 nodes per layer). Features dynamic performance graphs, training with a validation set, and C/C++ source code generation. For Sun (Solaris 1.x & 2.x, $499), PC (Windows 3.x, $199) Mac (System 7.x, $199) Floating point coprocessor required, Educational Discount, Money Back Guarantee, Muliti User Discount Windows Demo on: nic.funet.fi /pub/msdos/windows/demo oak.oakland.edu /pub/msdos/neural_nets gatordem.zip pkzip 2.04g archive file gatordem.txt readme text file 7. NeuroForecaster Name: NeuroForecaster(TM)/Genetica 3.1 Contact: Accel Infotech (S) Pte Ltd; 648 Geylang Road; Republic of Singapore 1438; Phone: +65-7446863; Fax: +65-7492467 accel@solomon.technet.sg For IBM PC 386/486 with mouse, or compatibles MS Windows* 3.1, MS DOS 5.0 or above 4 MB RAM, 5 MB available harddisk space min 3.5 inch floppy drive, VGA monitor or above, Math coprocessor recommended. Neuroforecaster 3.1 for Windows is priced at US$999 per single user license. For a limited period only Genetica is bundled free-of-charge. Please email us (accel@solomon.technet.sg) for order form. More information about NeuroForecaster(TM)/Genetical may be found in ftp.nus.sg incoming/accel. NeuroForecaster is a user-friendly neural network program specifically designed for building sophisticated and powerful forecasting and decision-support systems (Time-Series Forecasting, Cross-Sectional Classification, Indicator Analysis) Features: * GENETICA Net Builder Option for automatic network creation and optimization * 12 Neuro-Fuzzy Network Models * Multitasking & Background Training Mode * Unlimited Network Capacity * Rescaled Range Analysis & Hurst Exponent to Unveil Hidden Market Cycles & Check for Predictability * Correlation Analysis to Compute Correlation Factors to Analyze the Significance of Indicators * Weight Histogram to Monitor the Progress of Learning * Accumulated Error Analysis to Analyze the Strength of Input Indicators Its user-friendly interface allows the users to build applications quickly, easily and interactively, analyze the data visually and see the results immediately. The following example applications are included in the package: * Credit Rating - for generating the credit rating of bank loan applications * Stock market 6 monthly returns forecast * Stock selection based on company ratios * US$ to Deutschmark exchange rate forecast * US$ to Yen exchange rate forecast * US$ to SGD exchange rate forecast * Property price valuation * XOR - a classical problem to show the results are better than others * Chaos - Prediction of Mackey-Glass chaotic time series * SineWave - For demonstrating the power of Rescaled Range Analysis and significance of window size Techniques Implemented: * GENETICA Net Builder Option - network creation & optimization based on Darwinian evolution theory * Backprop Neural Networks - the most widely-used training algorithm * Fastprop Neural Networks - speeds up training of large problems even on slow machines * Radial Basis Function Networks - best for pattern classification problems * Neuro-Fuzzy Network - combines the power of neuro and fuzzy computing technologies * Rescaled Range Analysis - computes Hurst exponents to unveil hidden cycles & check for predictability * Correlation Analysis - to identify significant input indicators ------------------------------------------------------------------------ -A17.) Neural Network hardware ? [preliminary] [who will write some short comment on the most important HW-packages and chips ?] The Number 1 of each volume of the journal "Neural Networks" has a list of some dozens of suppliers of Neural Network support: Software, Hardware, Support, Programming, Design and Service. Here is a list of companies contributed by xli@computing-maths.cardiff.ac.uk: 1. HNC, INC. 5501 Oberlin Drive San Diego California 92121 (619) 546-8877 and a second address at 7799 Leesburg Pike, Suite 900 Falls Church, Virginia 22043 (703) 847-6808 Note: Australian Dist.: Unitronics Tel : (09) 4701443 Contact: Martin Keye HNC markets: 'Image Document Entry Processing Terminal' - it recognises handwritten documents and converts the info to ASCII. 'ExploreNet 3000' - a NN demonstrator 'Anza/DP Plus'- a Neural Net board with 25MFlop or 12.5M peak interconnects per second. 2. SAIC (Sience Application International Corporation) 10260 Campus Point Drive MS 71, San Diego CA 92121 (619) 546 6148 Fax: (619) 546 6736 3. Micro Devices 30 Skyline Drive Lake Mary FL 32746-6201 (407) 333-4379 MicroDevices makes MD1220 - 'Neural Bit Slice' Each of the products mentioned sofar have very different usages. Although this sounds similar to Intel's product, the architectures are not. 4. Intel Corp 2250 Mission College Blvd Santa Clara, Ca 95052-8125 Attn ETANN, Mail Stop SC9-40 (408) 765-9235 Intel is making an experimental chip: 80170NW - Electrically trainable Analog Neural Network (ETANN) It has 64 'neurons' on it - almost fully internally connectted and the chip can be put in an hierarchial architecture to do 2 Billion interconnects per second. Support software has already been made by California Scientific Software 10141 Evening Star Dr #6 Grass Valley, CA 95945-9051 (916) 477-7481 Their product is called 'BrainMaker'. 5. NeuralWare, Inc Penn Center West Bldg IV Suite 227 Pittsburgh PA 15276 They only sell software/simulator but for many platforms. 6. Tubb Research Limited 7a Lavant Street Peterfield Hampshire GU32 2EL United Kingdom Tel: +44 730 60256 7. Adaptive Solutions Inc 1400 NW Compton Drive Suite 340 Beaverton, OR 97006 U. S. A. Tel: 503 - 690 - 1236 FAX: 503 - 690 - 1249 8. NeuroDynamX, Inc. 4730 Walnut St., Suite 101B Boulder, CO 80301 Voice: (303) 442-3539 Fax: (303) 442-2854 Internet: techsupport@ndx.com NDX sells a number neural network hardware products: NDX Neural Accelerators: a line of i860-based accelerator cards for the PC that give up to 45 million connections per second for use with the DynaMind neural network software. iNNTS: Intel's 80170NX (ETANN) Neural Network Training System. NDX's president was one of the co-designers of this chip. And here is an incomplete list of Neurocomputers (provided by jon@kongle.idt.unit.no (Jon Gunnar Solheim)): Overview over known Neural Computers with their newest known reference. \subsection*{Digital} \subsubsection{Special Computers} {\bf AAP-2} Takumi Watanabe, Yoshi Sugiyama, Toshio Kondo, and Yoshihiro Kitamura. Neural network simulation on a massively parallel cellular array processor: AAP-2. In International Joint Conference on Neural Networks, 1989. {\bf ANNA} B.E.Boser, E.Sackinger, J.Bromley, Y.leChun, and L.D.Jackel.\\ Hardware Requirements for Neural Network Pattern Classifiers.\\ In {\it IEEE Micro}, 12(1), pages 32-40, February 1992. {\bf Analog Neural Computer} Paul Mueller et al. Design and performance of a prototype analog neural computer. In Neurocomputing, 4(6):311-323, 1992. {\bf APx -- Array Processor Accelerator}\\ F.Pazienti.\\ Neural networks simulation with array processors. In {\it Advanced Computer Technology, Reliable Systems and Applications; Proceedings of the 5th Annual Computer Conference}, pages 547-551. IEEE Comput. Soc. Press, May 1991. ISBN: 0-8186-2141-9. {\bf ASP -- Associative String Processor}\\ A.Krikelis.\\ A novel massively associative processing architecture for the implementation artificial neural networks.\\ In {\it 1991 International Conference on Acoustics, Speech and Signal Processing}, volume 2, pages 1057-1060. IEEE Comput. Soc. Press, May 1991. {\bf BSP400} Jan N.H. Heemskerk, Jacob M.J. Murre, Jaap Hoekstra, Leon H.J.G. Kemna, and Patrick T.W. Hudson. The bsp400: A modular neurocomputer assembled from 400 low-cost microprocessors. In International Conference on Artificial Neural Networks. Elsevier Science, 1991. {\bf BLAST}\\ J.G.Elias, M.D.Fisher, and C.M.Monemi.\\ A multiprocessor machine for large-scale neural network simulation. In {\it IJCNN91-Seattle: International Joint Conference on Neural Networks}, volume 1, pages 469-474. IEEE Comput. Soc. Press, July 1991. ISBN: 0-7883-0164-1. {\bf CNAPS Neurocomputer}\\ H.McCartor\\ Back Propagation Implementation on the Adaptive Solutions CNAPS Neurocomputer.\\ In {\it Advances in Neural Information Processing Systems}, 3, 1991. {\bf MA16 -- Neural Signal Processor} U.Ramacher, J.Beichter, and N.Bruls.\\ Architecture of a general-purpose neural signal processor.\\ In {\it IJCNN91-Seattle: International Joint Conference on Neural Networks}, volume 1, pages 443-446. IEEE Comput. Soc. Press, July 1991. ISBN: 0-7083-0164-1. {\bf Mindshape} Jan N.H. Heemskerk, Jacob M.J. Murre Arend Melissant, Mirko Pelgrom, and Patrick T.W. Hudson. Mindshape: a neurocomputer concept based on a fractal architecture. In International Conference on Artificial Neural Networks. Elsevier Science, 1992. {\bf mod 2} Michael L. Mumford, David K. Andes, and Lynn R. Kern. The mod 2 neurocomputer system design. In IEEE Transactions on Neural Networks, 3(3):423-433, 1992. {\bf NERV}\\ R.Hauser, H.Horner, R. Maenner, and M.Makhaniok.\\ Architectural Considerations for NERV - a General Purpose Neural Network Simulation System.\\ In {\it Workshop on Parallel Processing: Logic, Organization and Technology -- WOPPLOT 89}, pages 183-195. Springer Verlag, Mars 1989. ISBN: 3-5405-5027-5. {\bf NP -- Neural Processor}\\ D.A.Orrey, D.J.Myers, and J.M.Vincent.\\ A high performance digital processor for implementing large artificial neural networks.\\ In {\it Proceedings of of the IEEE 1991 Custom Integrated Circuits Conference}, pages 16.3/1-4. IEEE Comput. Soc. Press, May 1991. ISBN: 0-7883-0015-7. {\bf RAP -- Ring Array Processor }\\ N.Morgan, J.Beck, P.Kohn, J.Bilmes, E.Allman, and J.Beer.\\ The ring array processor: A multiprocessing peripheral for connectionist applications. \\ In {\it Journal of Parallel and Distributed Computing}, pages 248-259, April 1992. {\bf RENNS -- REconfigurable Neural Networks Server}\\ O.Landsverk, J.Greipsland, J.A.Mathisen, J.G.Solheim, and L.Utne.\\ RENNS - a Reconfigurable Computer System for Simulating Artificial Neural Network Algorithms.\\ In {\it Parallel and Distributed Computing Systems, Proceedings of the ISMM 5th International Conference}, pages 251-256. The International Society for Mini and Microcomputers - ISMM, October 1992. ISBN: 1-8808-4302-1. {\bf SMART -- Sparse Matrix Adaptive and Recursive Transforms}\\ P.Bessiere, A.Chams, A.Guerin, J.Herault, C.Jutten, and J.C.Lawson.\\ From Hardware to Software: Designing a ``Neurostation''.\\ In {\it VLSI design of Neural Networks}, pages 311-335, June 1990. {\bf SNAP -- Scalable Neurocomputer Array Processor} E.Wojciechowski.\\ SNAP: A parallel processor for implementing real time neural networks.\\ In {\it Proceedings of the IEEE 1991 National Aerospace and Electronics Conference; NAECON-91}, volume 2, pages 736-742. IEEE Comput.Soc.Press, May 1991. {\bf Toroidal Neural Network Processor}\\ S.Jones, K.Sammut, C.Nielsen, and J.Staunstrup.\\ Toroidal Neural Network: Architecture and Processor Granularity Issues.\\ In {\it VLSI design of Neural Networks}, pages 229-254, June 1990. {\bf SMART and SuperNode} P. Bessi`ere, A. Chams, and P. Chol. MENTAL : A virtual machine approach to artificial neural networks programming. In NERVES, ESPRIT B.R.A. project no 3049, 1991. (The report archived on neuroprose} \subsubsection{Standard Computers} {\bf EMMA-2}\\ R.Battiti, L.M.Briano, R.Cecinati, A.M.Colla, and P.Guido.\\ An application oriented development environment for Neural Net models on multiprocessor Emma-2.\\ In {\it Silicon Architectures for Neural Nets; Proceedings for the IFIP WG.10.5 Workshop}, pages 31-43. North Holland, November 1991. ISBN: 0-4448-9113-7. {\bf iPSC/860 Hypercube}\\ D.Jackson, and D.Hammerstrom\\ Distributing Back Propagation Networks Over the Intel iPSC/860 Hypercube}\\ In {\it IJCNN91-Seattle: International Joint Conference on Neural Networks}, volume 1, pages 569-574. IEEE Comput. Soc. Press, July 1991. ISBN: 0-7083-0164-1. {\bf SCAP -- Systolic/Cellular Array Processor}\\ Wei-Ling L., V.K.Prasanna, and K.W.Przytula.\\ Algorithmic Mapping of Neural Network Models onto Parallel SIMD Machines.\\ In {\it IEEE Transactions on Computers}, 40(12), pages 1390-1401, December 1991. ISSN: 0018-9340. ------------------------------------------------------------------------ -A19.) Databases for experimentation with NNs ? [are there any more ?] 1. The neural-bench Benchmark collection accessible via anonymous FTP on "ftp.cs.cmu.edu" [128.2.206.173] in directory "/afs/cs/project/connect/bench" In case of problems email contact is "neural-bench@cs.cmu.edu". The data sets in this repository include the 'nettalk' data, 'two spirals', protein structure prediction, vowel recognition, sonar signal classification, and a few others. 2. UCI machine learning database accessible via anonymous FTP on "ics.uci.edu" [128.195.1.1] in directory "/pub/machine-learning-databases" 3. NIST special databases of the National Institute Of Standards And Technology: NIST special database 2: Structured Forms Reference Set (SFRS) The NIST database of structured forms contains 5,590 full page images of simulated tax forms completed using machine print. THERE IS NO REAL TAX DATA IN THIS DATABASE. The structured forms used in this database are 12 different forms from the 1988, IRS 1040 Package X. These include Forms 1040, 2106, 2441, 4562, and 6251 together with Schedules A, B, C, D, E, F and SE. Eight of these forms contain two pages or form faces making a total of 20 form faces represented in the database. Each image is stored in bi-level black and white raster format. The images in this database appear to be real forms prepared by individuals but the images have been automatically derived and synthesized using a computer and contain no "real" tax data. The entry field values on the forms have been automatically generated by a computer in order to make the data available without the danger of distributing privileged tax information. In addition to the images the database includes 5,590 answer files, one for each image. Each answer file contains an ASCII representation of the data found in the entry fields on the corresponding image. Image format documentation and example software are also provided. The uncompressed database totals approximately 5.9 gigabytes of data. NIST special database 3: Binary Images of Handwritten Segmented Characters (HWSC) Contains 313,389 isolated character images segmented from the 2,100 full-page images distributed with "NIST Special Database 1". 223,125 digits, 44,951 upper-case, and 45,313 lower-case character images. Each character image has been centered in a separate 128 by 128 pixel region, error rate of the segmentation and assigned classification is less than 0.1%. The uncompressed database totals approximately 2.75 gigabytes of image data and includes image format documentation and example software. NIST special database 4: 8-Bit Gray Scale Images of Fingerprint Image Groups (FIGS) The NIST database of fingerprint images contains 2000 8-bit gray scale fingerprint image pairs. Each image is 512 by 512 pixels with 32 rows of white space at the bottom and classified using one of the five following classes: A=Arch, L=Left Loop, R=Right Loop, T=Tented Arch, W=Whirl. The database is evenly distributed over each of the five classifications with 400 fingerprint pairs from each class. The images are compressed using a modified JPEG lossless compression algorithm and require approximately 636 Megabytes of storage compressed and 1.1 Gigabytes uncompressed (1.6 : 1 compression ratio). The database also includes format documentation and example software. More short overview: Special Database 1 - NIST Binary Images of Printed Digits, Alphas, and Text Special Database 2 - NIST Structured Forms Reference Set of Binary Images Special Database 3 - NIST Binary Images of Handwritten Segmented Characters Special Database 4 - NIST 8-bit Gray Scale Images of Fingerprint Image Groups Special Database 6 - NIST Structured Forms Reference Set 2 of Binary Images Special Database 7 - NIST Test Data 1: Binary Images of Handprinted Segmented Characters Special Software 1 - NIST Scoring Package Release 1.0 Special Database 1 - $895.00 Special Database 2 - $250.00 Special Database 3 - $895.00 Special Database 4 - $250.00 Special Database 6 - $250.00 Special Database 7 - $1,000.00 Special Software 1 - $1,150.00 The system requirements for all databases are a 5.25" CD-ROM drive with software to read ISO-9660 format. Contact: Darrin L. Dimmick dld@magi.ncsl.nist.gov (301)975-4147 If you wish to order the database, please contact: Standard Reference Data National Institute of Standards and Technology 221/A323 Gaithersburg, MD 20899 (301)975-2208 or (301)926-0416 (FAX) 4. CEDAR CD-ROM 1: Database of Handwritten Cities, States, ZIP Codes, Digits, and Alphabetic Characters The Center Of Excellence for Document Analysis and Recognition (CEDAR) State University of New York at Buffalo announces the availability of CEDAR CDROM 1: USPS Office of Advanced Technology The database contains handwritten words and ZIP Codes in high resolution grayscale (300 ppi 8-bit) as well as binary handwritten digits and alphabetic characters (300 ppi 1-bit). This database is intended to encourage research in off-line handwriting recognition by providing access to handwriting samples digitized from envelopes in a working post office. Specifications of the database include: + 300 ppi 8-bit grayscale handwritten words (cities, states, ZIP Codes) o 5632 city words o 4938 state words o 9454 ZIP Codes + 300 ppi binary handwritten characters and digits: o 27,837 mixed alphas and numerics segmented from address blocks o 21,179 digits segmented from ZIP Codes + every image supplied with a manually determined truth value + extracted from live mail in a working U.S. Post Office + word images in the test set supplied with dic- tionaries of postal words that simulate partial recognition of the corresponding ZIP Code. + digit images included in test set that simulate automatic ZIP Code segmentation. Results on these data can be projected to overall ZIP Code recogni- tion performance. + image format documentation and software included System requirements are a 5.25" CD-ROM drive with software to read ISO- 9660 format. For any further information, including how to order the database, please contact: Jonathan J. Hull, Associate Director, CEDAR, 226 Bell Hall State University of New York at Buffalo, Buffalo, NY 14260 hull@cs.buffalo.edu (email) 5. AI-CD-ROM (see above under "other sources of information about NNs") 6. Time series archive Various datasets of time series (to be used for prediction learning problems) are available for anonymous ftp at ftp.santafe.edu in pub/Time-Series. For example: - fluctuations in a far-infrared laser - Physiological data of patients with sleep apnea - High frequency currency exchange rate data - Intensity of a white dwarf star - J.S. Bachs final (unfinished) fugue from "Die Kunst der Fuge" Some of the datasets were used in a prediction contest and are described in detail in the book "Time series prediction: Forecasting the future and understanding the past", edited by Weigend/Gershenfield, Proceedings Volume XV in the Santa Fe Institute Studies in the Sciences of Complexity series of Addison Wesley (1994). ------------------------------------------------------------------------ That's all folks. ======================================================================== Acknowledgements: Thanks to all the people who helped to get the stuff above into the posting. I cannot name them all, because I would make far too many errors then. :-> No ? Not good ? You want individual credit ? OK, OK. I'll try to name them all. But: no guarantee.... THANKS FOR HELP TO: (in alphabetical order of email adresses, I hope) Allen Bonde Accel Infotech Spore Pte Ltd Alexander Linden S.Taimi Ames Axel Mulder anderson@atc.boeing.com Davide Anguita Avraam Pouliakis Kim L. Blackwell Paul Bakker Jamshed Bharucha Yijun Cai L. Leon Campbell Yaron Danon David Ewing David DeMers Denni Rognvaldsson Donald Tveter Frank Schnorrenberg Gary Lawrence Murphy gaudiano@park.bu.edu Lee Giles Glen Clark Phil Goodman guy@minster.york.ac.uk Joerg Heitkoetter Ralf Hohenstein Jean-Denis Muller Jeff Harpster Jonathan Kamens JJ Merelo Jon Gunnar Solheim Josef Nelissen Kjetil.Noervaag@idt.unit.no Luke Koops William Mackeown Peter Marvit masud@worldbank.org Yoshiro Miyata Madhav Moganti Jyrki Alakuijala mrs@kithrup.com Maciek Sitnik R. Steven Rainwater Michael Plonski Lutz Prechelt [creator of FAQ] Richard Andrew Miles Outerbridge Richard Cornelius Rob Cunningham Robert.Kocjancic@IJS.si Osamu Saito Sheryl Cormicle Ted Stockwell Thomas G. Dietterich Thomas.Vogel@cl.cam.ac.uk Ulrich Wendl Matthew P Wiener Wesley Elsberry Bye Lutz -- Lutz Prechelt (email: prechelt@ira.uka.de) | Whenever you Institut fuer Programmstrukturen und Datenorganisation | complicate things, Universitaet Karlsruhe; 76128 Karlsruhe; Germany | they get (Voice: ++49/721/608-4068, FAX: ++49/721/694092) | less simple.