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.).
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.
|Auteur(s)||Simon Haykin, Bernard Widrow|
|Nb. de pages||490|
|Format||16 x 24|
|Intérieur||Noir et Blanc|
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