book announcement

[Laurene V. Fausett]

BOOK ANNOUNCEMENT

Title:  Fundamentals of Neural Networks:  Architectures, Algorithms, and
	Applications
Author:  Laurene V. Fausett
Publisher:  Prentice Hall
Ordering Information:
        Price   $49.00
        ISBN    0-13-334186-0
        To order, call Prentice-Hall Customer Service at 1-800-922-0579 or
your local Prentice-Hall representative.
        This book has also been published in paperback as a Prentice Hall
International Edition with ISBN 0-13-042250-9 for distribution outside of
the U.S.A., Canada, and Mexico.

Brief Description:
Written with the beginner in mind, this volume offers an exceptionally
clear and thorough introduction to neural networks at an elementary level. 
Systematic discussion of all major neural nets features presentation of the
architectures, detailed algorithms, and examples of simple applications -
in many cases variations on a theme.  Each chapter concludes with
suggestions for further study, including numerous exercises and computer
projects.  An instructor's manual with solutions and sample software (in
Fortran and C) will be available later this spring.

Table of Contents
Chapter 1  INTRODUCTION;  1.1  Why neural networks, and why now?;  
1.2  What is a neural net?;  1.3  Where are neural nets being used?;  
1.4  How are neural networks used?;  1.5  Who is developing neural networks?;  
1.6  When neural nets began - the McCulloch-Pitts neuron.
Chapter 2  SIMPLE NEURAL NETS FOR PATTERN CLASSIFICATION;  2.1  General
discussion;  2.2 Hebb net;  2.3  Perceptron;  2.4  Adaline.
Chapter 3  PATTERN ASSOCIATION;  3.1  Training algorithms for pattern
association;  3.2  Heteroassociative memory neural network;  
3.3  Autoassociative net;  3.4  Iterative autoassociative net;  
3.5  Bidirectional associative memory (BAM).
Chapter 4  NEURAL NETWORKS BASED ON COMPETITION;  4.1  Fixed-weight
competitive nets;  4.2  Kohonen self-organizing maps;  4.3  Learning vector
quantization;  4.4  Counterpropagation.
Chapter 5  ADAPTIVE RESONANCE THEORY;  5.1  Introduction;  5.2  ART1;  
5.3  ART2.
Chapter 6  BACKPROPAGATION NEURAL NET;  6.1  Standard backpropagation;  
6.2 Variations;  6.3  Theoretical results.
Chapter 7  A SAMPLER OF OTHER NEURAL NETS;  7.1  Fixed weight nets for
constrained optimization;  7.2  A few more nets that learn;  7.3  Adaptive
architectures;  7.4  Neocognitron.
Glossary;  References;  Index.