This volume provides an 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 should also find the book to be a useful reference. This second edition contains a substantial amount of additional material: two new chapters on nonlinear and subband adaptive filtering; linearly constrained Weiner filters and LMS algorithms; LMS algorithm behaviour in fast adaptation; affine projection algorithms; derivation smoothing; and 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. The volume should be of primary interest to those working in signal processing, communications and circuits and systems. It should also be of interest to those working in power systems, networks, learning systems and intelligent systems.
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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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