Analogue Imprecision In MLP Training

By: Peter Edwards, Alan F Murray

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Hardcover | 1 December 1996

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Hardcover
192 Pages

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22.86 x 15.88 x 1.91

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Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms. This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implication for learning and network performance. The aim of the book is to present a study of how including an imprecision model into a learning scheme as a "fault tolerance hint" can aid understanding of accuracy and precision requirements for a particular implementation. In addition the study shows how such a scheme can give rise to significant performance enhancement.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Computer Vision Engineering & Technology Electronics & Communications Engineering Electronics Engineering Circuits & Components Energy Technology & Engineering Electrical Engineering

Booktopia Publisher Services World Scientific Publishing Computer Networking & Communications

Preface
Introduction	p. 1
Neural Network Performance Metrics	p. 15
Noise in Neural Implementations	p. 37
Simulation Requirements and Environment	p. 63
Fault Tolerance	p. 81
Generalisation Ability	p. 99
Learning Trajectory and Speed	p. 121
Penalty Terms for Fault Tolerance	p. 137
Conclusions	p. 147
Fault Tolerance Hints - The General Case	p. 159
Bibliography	p. 165
Index	p. 173
Table of Contents provided by Blackwell. All Rights Reserved.

Analogue Imprecision In MLP Training

At a Glance

Hardcover

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Defective items

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