Adaptive Filter Theory / Edition 5

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Adaptive Filter Theory, 5e, is ideal for courses in Adaptive Filters.

Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fifth 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.

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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: 9780132671453
  • Publisher: Prentice Hall
  • Publication date: 6/6/2013
  • Edition description: New Edition
  • Edition number: 5
  • Pages: 912
  • Sales rank: 998,308
  • Product dimensions: 7.10 (w) x 9.20 (h) x 1.30 (d)

Meet the Author

Simon Haykin received his B.Sc. (First-class Honours), Ph.D., and D.Sc., all in Electrical Engineering from the University of Birmingham, England. He is a Fellow of the Royal Society of Canada, and a Fellow of the Institute of Electrical and Electronics Engineers. He is the recipient of the Henry Booker Gold Medal from URSI, 2002, the Honorary Degree of Doctor of Technical Sciences from ETH Zentrum, Zurich, Switzerland, 1999, and many other medals and prizes.

He is a pioneer in adaptive signal-processing with emphasis on applications in radar and communications, an area of research which has occupied much of his professional life.

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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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