Neural Computing - An Introduction

ISBN-13: 9780852742624 / Angielski / Miękka / 1990 / 256 str.

R. Beale; T. Jackson

Opis

This book explains the basic concepts of neural computation and is about the whole field of neural networks, covering all the major approaches and their important results; more especially, it is an introduction, developing the concepts and ideas from their simple basics through their formulation into powerful computational systems.

Recenzje

"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

Spis treści

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

O autorze

R Beale, T Jackson