ANALOGUE IMPRECISION IN MLP TRAINING, PROGRESS IN NEURAL PROCESSING, VOL 4 by P J Edwards

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Summary

Hardware inaccuracy and imprecision are important factors when implementing neural algorithms. This text presents a study of synaptic weight noise as a typical fault model for analogue VLSI realizations of MLP neural networks, and examines the implications for learning and network performance.

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ANALOGUE IMPRECISION IN MLP TRAINING, PROGRESS IN NEURAL PROCESSING, VOL 4 by P J Edwards

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 implications 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 afault 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.
SKU Unavailable
ISBN 13 9789810227395
ISBN 10 9810227396
Title ANALOGUE IMPRECISION IN MLP TRAINING, PROGRESS IN NEURAL PROCESSING, VOL 4
Author Peter Edwards
Series Progress In Neural Processing
Condition Unavailable
Binding Type Hardback
Publisher World Scientific Publishing Co Pte Ltd
Year published 1996-08-01
Number of pages 192
Cover note Book picture is for illustrative purposes only, actual binding, cover or edition may vary.