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Progress in Neural Processing - Vol. 4
ANALOGUE IMPRECISION IN MLP TRAINING
edited by P J Edwards & A F Murray (Univ. Edinburgh)
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.
Contents:
- Introduction
- Neural Network Performance Metrics
- Noise in Neural
Implementations
- Simulation Requirements and Environment
- Fault Tolerance
- Generalisation Ability
- Learning Trajectory and Speed
- Penalty Terms for Fault Tolerance
- Conclusions
- Fault Tolerance Hints — The General Case
- Bibliography
- Index
Readership: Scientists and researchers in neural networks and electrical &
electronic engineering.
| 192pp |
Pub. date: Aug 1996 |
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* Special price applies only to individuals purchasing online and cannot be used in conjunction with any other offers.
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