Least-Mean-Square Adaptive Filters

Simon Haykin, Bernard Widrow

490 pages, parution le 03/10/2003

Ajouter à une liste

Indisponible

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.

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