ISBN-10:
0471495174
ISBN-13:
9780471495178
Pub. Date:
10/16/2001
Publisher:
Wiley
Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability / Edition 1

Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability / Edition 1

Hardcover

Current price is , Original price is $245.0. You

Temporarily Out of Stock Online

Please check back later for updated availability.

Product Details

ISBN-13: 9780471495178
Publisher: Wiley
Publication date: 10/16/2001
Series: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control Series
Pages: 308
Product dimensions: 6.83(w) x 9.72(h) x 0.90(d)

About the Author

Danilo Mandic from the Imperial College London, London, UK was named Fellow of the Institute of Electrical and Electronics Engineers in 2013 for contributions to multivariate and nonlinear learning systems.

Jonathon A. Chambers is the author of Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, published by Wiley.

Table of 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.

Customer Reviews

Most Helpful Customer Reviews

See All Customer Reviews