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Least-Mean-Square Adaptive Filters

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Least-Mean-Square Adaptive Filters

Least-Mean-Square Adaptive Filters

Simon Haykin, Bernard Widrow

490 pages, parution le 03/10/2003

Résumé

A landmark text in LMS filter technology–from the field's leading authorities In the field of electrical engineering and signal processing, few algorithms have proven as adaptable as the least-mean-square (LMS) algorithm. Devised by Bernard Widrow and M. Hoff, this simple, yet effective algorithm now represents the cornerstone for the design of adaptive transversal (tapped-delay-line) filters.

Today, working efficiently with LMS adaptive filters not only involves understanding their fundamentals; it also means staying current with their many applications in practical systems. However, no single resource has presented an up-to-the-minute examination of these and all other essential aspects of LMS filters—until now.

Edited by Simon Haykin and Bernard Widrow, the original inventor of the technology, and, Least-Mean-Square Adaptive Filters offers the most definitive look at the LMS filter available anywhere. Here, you'll get a commanding perspective on the desirable properties that have made LMS filters the turnkey technology for adaptive signal processing. Just as importantly, Least-Mean-Square Adaptive Filters brings together the contributions of renowned experts whose insights reflect the state-of-the-art of the field today. In each chapter, the book presents the latest thinking on a wide range of vital, fast-emerging topics, including:

  • Traveling-wave analysis of long LMS filters
  • Energy conservation and the learning ability of LMS adaptive filters
  • Robustness of LMS filters
  • Dimension analysis for LMS filters
  • Affine projection filters
  • Proportionate adaptation
  • Dynamic adaptation
  • Error whitening Wiener filters

As the editors point out, there is no direct mathematical theory for the stability and steady-state performance of the LMS filter. But it is possible to chart its behavior in a stationary and nonstationary environment. Least-Mean-Square Adaptive Filters puts these defining characteristics into sharp focus, and—more than any other source—brings you up to speed on everything that the LMS filter has to offer.

Contents

  • Introduction (Simon Haykin).
  • On the Efficiency of Adaptive Algorithms (Berrnard Widrow and Max Kamenetsky).
  • Travelling-Wave Model of Long LMS Filters (Hans Butterweck).
  • Energy Conservation and the Learning Ability of LMS Adaptive Filters (Ali Sayed & Vitor H. Nascimento).
  • On the Robustness of LMS Filters (Babak Hassibi).
  • Dimension Analysis for Least-Mean-Square Algorithms (Iven M.Y. Mareels, et al.).
  • Control of LMS-Type Adaptive Filters (Eberhard Haensler and Gerhard Uwe Schmidt).
  • Affine Projection Algorithms (Steve Gay).
  • Proportionate Adaptation: New Paradigms in Adaptive Filters (Zhe Chen, et al.).
  • Steady-State Dynamic Weight Behavior in (N)LMS Adaptive Filters (A.A. (Louis) Beex and James R. Zeidler).
  • Error Whitening Wiener Filters: Theory and Algorithms (Jose Principe, et al.).
  • Index.

L'auteur - Simon Haykin

is University Professor and Director of the Adaptive Systems Laboratory at McMaster University (Ontario, Canada)

L'auteur - Bernard Widrow

PHD, is Professor for Adaptive Systems at Stanford University.

Caractéristiques techniques

  PAPIER
Éditeur(s) Wiley
Auteur(s) Simon Haykin, Bernard Widrow
Parution 03/10/2003
Nb. de pages 490
Format 16 x 24
Couverture Relié
Poids 835g
Intérieur Noir et Blanc
EAN13 9780471215707
ISBN13 978-0-47-121570-7

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