Review
From the reviews of the third edition: "This text concentrates on studying on-line algorithms, those whose adaptation occurs whenever a new sample of each environment signal is available. … This edition also includes basic introductions to nonlinear adaptive filtering and blind signal processing as natural extensions of the algorithms … . The book is of great importance for digital signal processing undergraduate and graduate courses in universities." (George S. Stavrakakis, Zentralblatt MATH, Vol. 1155, 2009) "The book titled ‘Adaptive Filtering: Algorithms and Practical Implementation,’ Third Edition, by Paulo S. R. Diniz replaces two previous editions. This new edition has improved significantly upon those two editions. … The book is also very practical. To attest to its suitability as a teaching text, the author has given a number of carefully developed examples which are useful … to students. … Overall, the book is excellent for teaching the subject of adaptive signal processing and enhances the landscape of textbooks on the subject." (Tokunbo Ogunfunmi, IEEE Communications Magazine, October, 2009)
--This text refers to the
Paperback
edition.
Product Description
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.