Analogue imprecision in MLP training
Author(s)
Bibliographic Information
Analogue imprecision in MLP training
（Progress in neural processing, 4）
World Scientific, c1996
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
- Neural network performance metrics
- noise in neural implementations
- simulation requirements and environment
- fault tolerance
- generalisation ability
- learning trajectory and speed.
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