Guide to Neural Computing Applications

Author: Tarassenko   Lionel  

Publisher: Elsevier Science‎

Publication year: 1998

E-ISBN: 9780080512600

P-ISBN(Paperback): 9780340705896

P-ISBN(Hardback):  9780340705896

Subject: F224-39 computer applications;TP3 Computers

Language: ENG

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Description

Neural networks have shown enormous potential for commercial exploitation over the last few years but it is easy to overestimate their capabilities. A few simple algorithms will learn relationships between cause and effect or organise large volumes of data into orderly and informative patterns but they cannot solve every problem and consequently their application must be chosen carefully and appropriately.

This book outlines how best to make use of neural networks. It enables newcomers to the technology to construct robust and meaningful non-linear models and classifiers and benefits the more experienced practitioner who, through over familiarity, might otherwise be inclined to jump to unwarranted conclusions. The book is an invaluable resource not only for those in industry who are interested in neural computing solutions, but also for final year undergraduates or graduate students who are working on neural computing projects. It provides advice which will help make the best use of the growing number of commercial and public domain neural network software products, freeing the specialist from dependence upon external consultants.

Chapter

Front Cover

pp.:  1 – 4

Copyright Page

pp.:  5 – 6

Contents

pp.:  6 – 10

Foreword

pp.:  10 – 12

Chapter 1. Introduction

pp.:  12 – 16

Chapter 2. Mathematical background for neural computing

pp.:  16 – 48

Chapter 3. Managing a neural computing project

pp.:  48 – 60

Chapter 4. Identifying applications and assessing their feasibility

pp.:  60 – 70

Chapter 5. Neural computing hardware and software

pp.:  70 – 78

Chapter 6. Collecting and preparing data

pp.:  78 – 88

Chapter 7. Design, training and testing of the prototype

pp.:  88 – 110

Chapter 8. The case studies

pp.:  110 – 132

Chapter 9. More advanced topics

pp.:  132 – 140

Appendix A: The error back-propagation algorithm for weight updates in an MLP

pp.:  140 – 142

Appendix B: Use of Bayes' theorem to compensate for different prior probabilities

pp.:  142 – 144

References

pp.:  144 – 148

Index

pp.:  148 – 152

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