Neural Computing - An Introduction: 1st Edition (Paperback) book cover

Neural Computing - An Introduction

1st Edition

By R Beale, T Jackson

CRC Press

256 pages

Purchasing Options:$ = USD
Paperback: 9780852742624
pub: 1990-01-01
$68.95
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Hardback: 9781138413092
pub: 2017-07-27
$195.00
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Description

Neural computing is one of the most interesting and rapidly growing areas of research, attracting researchers from a wide variety of scientific disciplines. Starting from the basics, Neural Computing covers all the major approaches, putting each in perspective in terms of their capabilities, advantages, and disadvantages. The book also highlights the applications of each approach and explores the relationships among models developed and between the brain and its function.

A comprehensive and comprehensible introduction to the subject, this book is ideal for undergraduates in computer science, physicists, communications engineers, workers involved in artificial intelligence, biologists, psychologists, and physiologists.

Reviews

"Neural Computing is easy on the eye with a good layout and use of graphical icons to draw attention to mathematical proofs, algorithms (in clear format, which would lend itself to computer implementation), and summary sections."

-Denise Gorse, Times Higher Education Supplement

"… most accessible. … I was most impressed with the quality of this book. … hard pressed to beat …"

-David Williams, The Australian Computer Journal

"It is clear that any introductory book must explain what the leaders of the current revival have done. This is well done by Beale and Jackson."

-Igor Aleksander, Imperial College of Science, Technology and Medicine, London, UK

Table of Contents

INTRODUCTION

Humans and computers

The structure of the brain

Learning in machines

The differences

Summary

PATTERN RECOGNITION

Introduction

Pattern recognition in perspective

Pattern recognition-a definition

Feature vectors and feature space

Discriminant functions

Classification techniques

Linear classifiers

Statistical techniques

Pattern recognition-a summary

THE BASIC NEURON

Introduction

Modeling the single neuron

Learning in simple neurons

The perceptron: a vectorial perspective

The perceptron learning rule: proof

Limitations of perceptrons

The end of the line?

Summary

THE MULTILAYER PERCEPTRON

Introduction

Altering the perceptron model

The new model

The new learning rule

The multilayer perceptron algorithm

The XOR problem revisited

Visualizing network behavior

Multilayer perceptrons as classifiers

Generalization

Fault tolerance

Learning difficulties

Radial basis functions

Applications

Summary

KOHONEN SELF-ORGANIZING NETWORKS

Introduction

The Kohonen algorithm

Weight training

Neighborhoods

Reducing the neighborhood

Learning vector quantization (LVQ)

The phonetic typewriter

Summary

HOPFIELD NETWORKS

Introduction

The Hopfield model

The energy landscape

The Boltzmann machine

Constraint satisfaction

Summary

ADAPTIVE RESONANCE THEORY

Introduction

Adaptive resonance theory (ART)

Architecture and operation

ART algorithm

Training the ART network

Classification

Conclusion

Summary of ART

ASSOCIATIVE MEMORY

Standard computer memory

Implementing associative memory

Implementation in RAMs

RAMs and N-tupling

Willshaw's associative net

The ADAM system

Kanerva's sparse distributed memory

Bidirectional associative memories

Conclusion

Summary

INTO THE LOOKING GLASS

Overview

Hardware and software implementations

Optical computing

Optical computing and neural networks

INDEX

Subject Categories

BISAC Subject Codes/Headings:
MAT000000
MATHEMATICS / General
MAT021000
MATHEMATICS / Number Systems
SCI040000
SCIENCE / Mathematical Physics