Adaptive Filter Theory / Edition 3

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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


  • 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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Editorial Reviews

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 (
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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
  • Edition number: 3
  • Pages: 987
  • Product dimensions: 7.73 (w) x 9.51 (h) x 1.69 (d)

Table of Contents

Introduction 1
Ch. 1 Discrete-Time Signal Processing 79
Ch. 2 Stationary Processes and Models 96
Ch. 3 Spectrum Analyis 136
Ch. 4 Eigenanalysis 160
Ch. 5 Wiener Filters 194
Ch. 6 Linear Prediction 241
Ch. 7 Kalman Filters 302
Ch. 8 Method of Steepest Descent 339
Ch. 9 Least-Mean-Square Algorithm 365
Ch. 10 Frequency-Domain Adaptive Filters 445
Ch. 11 Method of Least Squares 483
Ch. 12 Rotations and Reflections 536
Ch. 13 Recursive Least-Squares Algorithm 562
Ch. 14 Square-Root Adaptive Filters 589
Ch. 15 Order-Recursive Adaptive Filters 630
Ch. 16 Tracking of Time-Varying Systems 701
Ch. 17 Fine-Precision Effects 738
Ch. 18 Blind Deconvolution 772
Ch. 19 Back-Propagation Learning 817
Ch. 20 Radial Basis Funuction Networks 855
Appendix A Complex Variables 875
Appendix B Differentiation with Respect to a Vector 890
Appendix C Method of Lagrange Multipliers 895
Appendix D Estimation Theory 899
Appendix E Maximum-Entropy Method 905
Appendix F Minimum-Variance Distortionless Response Spectrum 912
Appendix G Gradient Adaptive Lattice Algorithm 915
Appendix H Solution of the Difference Equation (9.75) 919
Appendix I Steady-State Analysis of the LMS Algorithm without Invoking the Independence Assumption 921
Appendix J The Complex Wishart Distribution 924
Glossary 928
Abbreviations 932
Principal Symbols 933
Bibliography 941
Index 978
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