Analogue Imprecision In Mlp Training, Progress In Neural Processing, Vol 4
eBook - PDF

Analogue Imprecision In Mlp Training, Progress In Neural Processing, Vol 4

  1. 192 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Analogue Imprecision In Mlp Training, Progress In Neural Processing, Vol 4

About this book

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.

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Yes, you can access Analogue Imprecision In Mlp Training, Progress In Neural Processing, Vol 4 by Peter Edwards, Alan F Murray in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Science General. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Contents
  2. Preface
  3. Chapter 1 Introduction
  4. Chapter 2 Neural Network Performance Metrics
  5. Chapter 3 Noise in Neural Implementations
  6. Chapter 4 Simulation Requirements and Environment
  7. Chapter 5 Fault Tolerance
  8. Chapter 6 Generalisation Ability
  9. Chapter 7 Learning Trajectory and Speed
  10. Chapter 8 Penalty Terms for Fault Tolerance
  11. Chapter 9 Conclusions
  12. Appendix A Fault Tolerance Hints — The General Case
  13. Bibliography
  14. Index