Adaptive Filter Theory / Edition 3

Adaptive Filter Theory / Edition 3

by Simon Haykin, Simon Haykin
     
 

ISBN-10: 013322760X

ISBN-13: 9780133227604

Pub. Date: 12/27/1995

Publisher: Prentice Hall Professional Technical Reference

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

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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

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Product Details

ISBN-13:
9780133227604
Publisher:
Prentice Hall Professional Technical Reference
Publication date:
12/27/1995
Series:
Prentice Hall Information and System Science Series
Edition description:
Older Edition
Pages:
987
Product dimensions:
7.73(w) x 9.51(h) x 1.69(d)

Table of Contents

Preface
Acknowledgments
Introduction1
Ch. 1Discrete-Time Signal Processing79
Ch. 2Stationary Processes and Models96
Ch. 3Spectrum Analyis136
Ch. 4Eigenanalysis160
Ch. 5Wiener Filters194
Ch. 6Linear Prediction241
Ch. 7Kalman Filters302
Ch. 8Method of Steepest Descent339
Ch. 9Least-Mean-Square Algorithm365
Ch. 10Frequency-Domain Adaptive Filters445
Ch. 11Method of Least Squares483
Ch. 12Rotations and Reflections536
Ch. 13Recursive Least-Squares Algorithm562
Ch. 14Square-Root Adaptive Filters589
Ch. 15Order-Recursive Adaptive Filters630
Ch. 16Tracking of Time-Varying Systems701
Ch. 17Fine-Precision Effects738
Ch. 18Blind Deconvolution772
Ch. 19Back-Propagation Learning817
Ch. 20Radial Basis Funuction Networks855
Appendix A Complex Variables875
Appendix B Differentiation with Respect to a Vector890
Appendix C Method of Lagrange Multipliers895
Appendix D Estimation Theory899
Appendix E Maximum-Entropy Method905
Appendix F Minimum-Variance Distortionless Response Spectrum912
Appendix G Gradient Adaptive Lattice Algorithm915
Appendix H Solution of the Difference Equation (9.75)919
Appendix I Steady-State Analysis of the LMS Algorithm without Invoking the Independence Assumption921
Appendix J The Complex Wishart Distribution924
Glossary928
Abbreviations932
Principal Symbols933
Bibliography941
Index978

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