Haykin, S. (1994). Neural Networks, a Comprehensive Foundation.

Macmillan, New York, NY. "A very readable, well written intermediate to advanced text on NNs Perspective is primarily one of pattern recognition, estimation and signal processing. However, there are well-written chapters on neurodynamics and VLSI implementation. Though there is emphasis on formal mathematical models of NNs as universal approximators, statistical estimators, etc., there are also examples of NNs used in practical applications. The problem sets at the end of each chapter nicely complement the material. In the bibliography are over 1000 references. If one buys only one book on neural networks, this should be it."

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."

Masters,Timothy (1994). Practical Neural Network Recipes in C++. Academic Press, ISBN 0-12-479040-2, US $45 incl. disks.

"Lots of very good practical advice which most other books lack."

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".

Fausett, L. V. (1994). Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall, ISBN 0-13-334186-0. Also published as a Prentice Hall International Edition, ISBN 0-13-042250-9.

Sample softeware (source code listings in C and Fortran) is included in an Instructor's Manual. "Intermediate in level between Wasserman and Hertz/Krogh/Palmer. Algorithms for a broad range of neural networks, including a chapter on Adaptive Resonace Theory with ART2. Simple examples for each network."

Freeman, James (1994). Simulating Neural Networks with Mathematica, Addison-Wesley, ISBN: 0-201-56629-X.

Helps the reader make his own NNs. The mathematica code for the programs in the book is also available through the internet: Send mail to MathSource@wri.com or try http://www.wri.com/ on the World Wide Web.

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."

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"; "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".

Rao, V.B & H.V. (1993). C++ Neural Networks and Fuzzy Logic. MIS:Press, ISBN 1-55828-298-x, US $45 incl. disks.

"Probably not 'leading edge' stuff but detailed enough to get your hands dirty!"

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.

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".

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".

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".

Cichocki, A. and Unbehauen, R. (1994). Neural Networks for Optimization and Signal Processing. John Wiley & Sons, West Sussex, England, 1993, ISBN 0-471-930105 (hardbound), 526 pages, $57.95.

"Partly a textbook and partly a research monograph; introduces the basic concepts, techniques, and models related to neural networks and optimization, excluding rigorous mathematical details. Accessible to a wide readership with a differential calculus background. The main coverage of the book is on recurrent neural networks with continuous state variables. The book title would be more appropriate without mentioning signal processing. Well edited, good illustrations."

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 andElectronic 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".

Origin: the comp.ai.neural-nets newsgroup.