Haykin examines both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. This edition has been updated and refined to keep current with the field and develop concepts in as unified and accessible a manner as possible. It: introduces a completely new chapter on Frequency-Domain Adaptive Filters; adds a chapter on Tracking Time-Varying Systems; adds two chapters on Neural Networks; enhances material on RLS algorithms; strengthens linkages to Kalman filter theory to gain a more unified treatment of the standard, square-root and order-recursive forms; and includes new computer experiments using MATLAB software that illustrate the underlying theory and applications of the LMS and RLS algorithms.

Quatri�me de couverture

CONTENTS

Preface
Acknowledgments
Background and Preview

Chapter 1 Stochastic Processes and Models
Chapter 2 Wiener Filters
Chapter 3 Linear Prediction
Chapter 4 Method of Steepest Descent
Chapter 5 Least-Mean-Square Adaptive Filters
Chapter 6 Normalized Least-Mean-Square Adaptive Filters
Chapter 7 Frequency-Domain and Subband Adaptive Filters
Chapter 8 Method of Least Squares
Chapter 9 Recursive Least-Square Adaptive Filters
Chapter 10 Kalman Filters
Chapter 11 Square-Root Adaptive Filters
Chapter 12 Order-Recursive Adaptive Filters
Chapter 13 Finite-Precision Effects
Chapter 14 Tracking of Time-Varying Systems
Chapter 15 Adaptive Filters Using Infinite-Duration Impulse Response Structures
Chapter 16 Blind Deconvolution
Chapter 17 Back-Propagation Learning

Epilogue

Appendix A Complex Variables
Appendix B Differentiation with Respect to a Vector
Appendix C Method of Lagrange Multipliers
Appendix D Estimation Theory
Appendix E Eigenanalysis
Appendix F Rotations and Reflections
Appendix G Complex Wishart Distribution
Glossary

Bibliography

Index

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

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