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Other titles in the Adaptive and Learning Systems for Signal Processing, Communications and Control series:
Least-Mean-Square Adaptive Filters (Adaptive and Learning Systems for Signal Processing, Communications and Control)by Simon Haykin
Synopses & Reviews
A landmark text in LMS filter technology from the fields 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 filtersuntil now.
Edited by Simon Haykin and Bernard Widrow, the original inventor of the technology, Least-Mean-Square Adaptive Filters offers the most definitive look at the LMS filter available anywhere. Here, readers will 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:
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, andmore than any other sourcebrings you up to speed on everything that the LMS filter has to offer.
Book News Annotation:
The least-mean-square (LMS) algorithm represents the cornerstone for the design of adaptive transversal filters. Haykin (director, Adaptive Systems Laboratory, McMaster University), and Widrow (adaptive systems, Stanford University), one of the original inventors of the algorithm, look at properties that have made LMS filters the turnkey technology for adaptive signal processing, and bring together contributors in communication technology, electrical engineering, and computational neuroengineering whose insights reflect the state of the art in the field. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com)
* The only book to cover these topics together.
About the Author
SIMON HAYKIN, PhD, is University Professor and Director of the Adaptive Systems Laboratory at McMaster University.
BERNARD WIDROW, PhD, is Professor for Adaptive Systems at Stanford University.
Table of Contents
Introduction (Simon Haykin).
1. On the Efficiency of Adaptive Algorithms (Berrnard Widrow and Max Kamenetsky).
2. Travelling-Wave Model of Long LMS Filters (Hans Butterweck).
3. Energy Conservation and the Learning Ability of LMS Adaptive Filters (Ali Sayed & Vitor H. Nascimento).
4. On the Robustness of LMS Filters (Babak Hassibi).
5. Dimension Analysis for Least-Mean-Square Algorithms (Iven M.Y. Mareels, et al.).
6. Control of LMS-Type Adaptive Filters (Eberhard Haensler and Gerhard Uwe Schmidt).
7. Affine Projection Algorithms (Steve Gay).
8. Proportionate Adaptation: New Paradigms in Adaptive Filters (Zhe Chen, et al.).
9. Steady-State Dynamic Weight Behavior in (N)LMS Adaptive Filters (A.A. (Louis) Beex and James R. Zeidler).
10. Error Whitening Wiener Filters: Theory and Algorithms (Jose Principe, et al.).
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