Recurrent Neural Networks for Prediction
Learning Algorithms, Architectures and Stability
Danilo Mandic, University of Bath, UK and Jonathan Chambers, University of Bath, UK


0471 49517 4 August 2001 Hardback 304pp



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Demonstrating how recurrent neural networks can be implemented to expand the range of traditional signal processing techniques, this book covers the background of existing approaches along with new experimental evidence.
  • Features original research on stability in neural networks
  • Combines rigorous mathematical analysis with application examples
  • Covers experimental evidence and existing approaches
Postgraduates and research engineers from a broad range of disciplines, including signal processing, neural networks, communications, nonlinear control and time series analysis would find this text to be a valuable reference resource.

Contents:

  • Preface
  • Introduction
  • Fundamentals
  • Network Architectures for Prediction
  • Activation Functions Used in Neural Networks
  • Recurrent Neural Networks Architectures
  • Neural Networks as Nonlinear Adaptive Filters
  • Stability Issues in RNN Architectures
  • Data-Reusing Adaptive Learning Algorithms
  • A Class of Normalised Algorithms for Online Training of Recurrent Neural Networks
  • Convergence of Online Learning Algorithms in Neural Networks
  • Some Practical Considerations of Predictability and Learning Algorithms for Various Signals
  • Exploiting Inherent Relationships Between Parameters in Recurrent Neural Networks
  • Appendix A The O Notation and Vector and Matrix Differentiation
  • Appendix B Concepts from the Approximation Theory
  • Appendix C Complex Sigmoid Activation Functions, Holomorphic Mappings and Modular Groups
  • Appendix D Learning Algorithms for RNNs
  • Appendix E Terminology Used in the Field of Neural Networks
  • Appendix F On the A Posteriori Approach in Science and Engineering
  • Appendix G Contraction Mapping Theorems
  • Appendix H Linear GAS Relaxation
  • Appendix I The Main Notions in Stability Theory
  • Appendix J Deasonsonalising Time Series
  • References
  • Index
Wiley Series on Adaptive Learning Systems for Signal Processing, Communications and Control


Copyright © 2000 John Wiley & Sons Ltd