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Neural computing: theory and practiceApril 1989
- Author:
- P. D. Wasserman
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Index Terms
- Neural computing: theory and practice
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Reviews
Jiri Horejs
This introductory book for nonspecialists has ten chapters and three appendices and covers perceptrons, backpropagation, counterpropagation, Boltzmann/Cauchy machines, Hopfield nets, bidirectional associative memories, adaptive resonance theory , and the cognitron and neocognitron; optical and biological networks are also briefly described. The approach is elementary; no higher mathematics is required. The style of presentation recalls Lippman's excellent survey paper [1] but is more elaborate and complete, though not always more lucid. The book brings pregnant formulations and realistic assessments of basic ideas, up-to-date references, and brief comments on properties concerning stability, parameter settings, etc. On the other hand, the algorithms are not stated explicitly (as in Lippman [1]), and some formulas are described rather than explained (for example, backpropagation). Each chapter is intended to be self-contained, so there are some deliberate repetitions; as there is no systematic overview of various application areas, however, the reader has no hint of where to focus her or his attention. It is surprising that the author, who is president of Anza Research, Inc., discusses future technologies without mentioning present implementation issues (accelerators, netware products, etc.). In conclusion, the reader will find a good explanation of general ideas but less help when trying to cross the bridge to real applications.
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