Physics of Neural Networks

An Introduction

By: Berndt Müller, Joachim Reinhardt, Michael T. Strickland

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Paperback | 2 October 1995 | Edition Number 2

At a Glance

Paperback
356 Pages

Edition Type
Revised

Dimensions(cm)
24.13 x 16.51 x 2.54

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Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.

Industry Reviews

"I have enjoyed using the previous edition of this well-known book both as a personal text and as a class manual. Although it claims to be only an introduction, it contains a wealth of material and addresses real problems in physics." Computing Reviews

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The Structure of the Central Nervous System	p. 3
Neural Networks Introduced	p. 13
Associative Memory	p. 24
Stochastic Neurons	p. 38
Cybernetic Networks	p. 46
Multilayered Perceptrons	p. 52
Applications	p. 63
More Applications of Neural Networks	p. 72
Network Architecture and Generalization	p. 93
Associative Memory: Advanced Learning Strategies	p. 108
Combinatorial Optimization	p. 126
VLSI and Neural Networks	p. 135
Symmetrical Networks with Hidden Neurons	p. 144
Coupled Neural Networks	p. 151
Unsupervised Learning	p. 162
Evolutionary Algorithms for Learning	p. 174
Statistical Physics and Spin Glasses	p. 191
The Hopfield Network for [actual symbol not reproducible]	p. 201
The Hopfield Network for Finite p/N	p. 209
The Space of Interactions in Neural Networks	p. 231
Numerical Demonstrations	p. 249
ASSO: Associative Memory	p. 253
ASSCOUNT: Associative Memory for Time Sequences	p. 264
PERBOOL: Learning Boolean Functions with Back-Prop	p. 268
PERFUNC: Learning Continuous Functions with Back-Prop	p. 275
Solution of the Traveling-Salesman Problem	p. 279
KOHOMAP: The Kohonen Self-organizing Map	p. 291
BTT: Back-Propagation Through Time	p. 296
Neurogen: Using Genetic Algorithms to Train Networks	p. 303
References	p. 307
Index	p. 325
Table of Contents provided by Blackwell. All Rights Reserved.

Physics of Neural Networks

An Introduction

At a Glance

Paperback

Industry Reviews

Shipping

How to return your order

Defective items

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