Adaptive Filter Theory

Adaptive Filter Theory

5.0 1
by Simon Haykin
     
 

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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

Overview

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

Editorial Reviews

Booknews
At a level suitable for graduate courses on adaptive signal processing, this textbook develops the mathematical theory of various realizations of linear adaptive filters with finite-duration impulse response, and also provides an introductory treatment of supervised neural networks. Numerous computer experiments illustrate the underlying theory and applications of the LMS (least mean-square) and RLS (recursive-least-squares) algorithms, and problems conclude each chapter. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Product Details

ISBN-13:
9780273764083
Publisher:
Pearson Education
Publication date:
11/28/2011

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