Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fourth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.

From the Back Cover

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

"About this title" may belong to another edition of this title.

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