Analogue imprecision in MLP training
- Responsibility
- Peter J. Edwards, Alan F. Murray.
- Imprint
- Singapore ; River Edge, NJ : World Scientific, ©1996.
- Physical description
- 1 online resource (xi, 178 pages) : illustrations
- Series
- Progress in neural processing ; 4.
Online
Available online
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Description
Creators/Contributors
- Author/Creator
- Edwards, Peter J. (Peter John)
- Contributor
- Murray, Alan F.
Contents/Summary
- Bibliography
- Includes bibliographical references (pages 165-172) and index.
- Contents
-
- Neural network performance metrics
- noise in neural implementations
- simulation requirements and environment
- fault tolerance
- generalisation ability
- learning trajectory and speed.
- (source: Nielsen Book Data)
- Publisher's summary
-
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 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.
(source: Nielsen Book Data)
Subjects
Bibliographic information
- Publication date
- 1996
- Series
- Progress in neural processing ; 4
- ISBN
- 9789812830012 (electronic bk.)
- 9812830014 (electronic bk.)
- 9810227396
- 9789810227395
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