Analogue imprecision in MLP training
著者
書誌事項
Analogue imprecision in MLP training
（Progress in neural processing, 4）
World Scientific, c1996
大学図書館所蔵 全3件
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
- Neural network performance metrics
- noise in neural implementations
- simulation requirements and environment
- fault tolerance
- generalisation ability
- learning trajectory and speed.
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