Adaptive Filtering: Algorithms and Practical Implementation

Paulo S.R. Diniz

Adaptive Filtering: Algorithms and Practical Implementation
  • 出版商: Kluwer Academic Publ
  • 出版日期: 2002-05-01
  • 售價: $960
  • 貴賓價: 9.8 折 $940
  • 語言: 英文
  • 頁數: 592
  • 裝訂: Hardcover
  • ISBN: 1402071256
  • ISBN-13: 9781402071256
  • 相關分類: Algorithms-data-structures

    • 已絕版

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商品描述

Adaptive Filtering: Algorithms and Practical Implementation, Second Edition, presents a concise overview of adaptive filtering, covering as many algorithms as possible in a unified form that avoids repetition and simplifies notation. It is suitable as a textbook for senior undergraduate or first-year graduate courses in adaptive signal processing and adaptive filters. The philosophy of the presentation is to expose the material with a solid theoretical foundation, to concentrate on algorithms that really work in a finite-precision implementation, and to provide easy access to working algorithms. Hence, practicing engineers and scientists will also find the book to be an excellent reference.

This second edition contains a substantial amount of new material:
  • Two new chapters on nonlinear and subband adaptive filtering;
  • Linearly constrained Weiner filters and LMS algorithms;
  • LMS algorithm behavior in fast adaptation;
  • Affine projection algorithms;
  • Derivation smoothing;
  • MATLAB codes for algorithms.

An instructor's manual, a set of master transparencies, and the MATLAB codes for all of the algorithms described in the text are also available. Useful to both professional researchers and students, the text includes 185 problems; over 38 examples, and over 130 illustrations. It is of primary interest to those working in signal processing, communications, and circuits and systems. It will also be of interest to those working in power systems, networks, learning systems, and intelligent systems.

Contents

Preface. 1. Introduction to Adaptive Filtering. 2. Fundamentals of Adaptive Filtering. 3. The Least-Mean-Square (LMS) Algorithm. 4. LMS-Based Algorithms. 5. Conventional RLS Adaptive Filter. 6. Adaptive Lattice-Based RLS Algorithms. 7. Fast Transversal RLS Algorithms. 8. QR-Decomposition-Based RLS Filters. 9. Adaptive IIR Filters. 10. Nonlinear Adaptive Filtering. 11. Subband Adaptive Filters. A. Quantization Effects in the LMS and RLS Algorithms. Index.

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