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

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

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

(NOTE: Each chapter ends with Summary and Discussion, and Problems.)
Introduction.

I. BACKGROUND MATERIAL.

1. Discrete-Time Signal Processing
2. Stationary Processes and Models
3. Spectrum Analysis
4. Eigenanalysis.

II. LINEAR OPTIMUM FILTERING.

5. Wiener Filters
6. Linear Prediction
7. Kalman Filters.

III. LINEAR ADAPTIVE FILTERING.

8. Method of Steepest Descent
9. Least-Mean Square Algorithm
10. Frequency-Domain Adaptive Filters
11. Method of Least Squares
12. Rotations and Reflections
13. Recursive Least-Squares Algorithm
14. Square-Root Adaptive Filters
15. Order-Recursive Adaptive Filters
16. Tracking of Time-Varying Systems
17. Finite-Precision Effects.

IV. NONLINEAR ADAPTIVE FILTERING.

18. Blind Deconvolution
19. Back-Propagation Learning
20. Radial Basis Function Networks
Appendix A: Complex Variables
Appendix B: Differentiation with Respect to a Vector
Appendix C: Method and Lagrange Multipliers
Appendix D: Estimation Theory
Appendix E: Maximum-Entropy Method
Appendix F: Minimum-Variance Distortionless Response Spectrum
Appendix G: Gradient Adaptive Lattice Algorithm
Appendix H: Solution of the Difference Equation (9.75)
Appendix I: Steady-State Analysis of the LMS Algorithm without Invoking the Independence Assumption
Appendix J: The Complex Wishart Distribution
Glossary
Abbreviations
Principal Symbols
Bibliograghy
Index.

Customer Reviews

Average Review:

Write a Review

and post it to your social network

     

Most Helpful Customer Reviews

See all customer reviews >