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Theory of affine projection algorithms for adaptive filtering

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Theory of affine projection algorithms for adaptive filtering/ by Kazuhiko Ozeki.
Author:	Ozeki, Kazuhiko.
Published:	Tokyo :Springer Japan : : 2016.,
Description:	xii, 223 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Adaptive filters. -
Online resource:	http://dx.doi.org/10.1007/978-4-431-55738-8
ISBN:	9784431557388

Theory of affine projection algorithms for adaptive filtering
Ozeki, Kazuhiko.

Theory of affine projection algorithms for adaptive filtering[electronic resource] /by Kazuhiko Ozeki. - Tokyo :Springer Japan :2016. - xii, 223 p. :ill., digital ;24 cm. - Mathematics for industry,v.222198-350X ;. - Mathematics for industry ;v.9..

Introduction -- Classical Adaptation Algorithms -- Affine Projection Algorithm -- Family of Affine Projection Algorithms -- Convergence Behavior of APA -- Reduction of Computational Complexity -- Kernel Affine Projection Algorithm -- Variable Parameter APAs -- Appendix; Matrices.

This book focuses on theoretical aspects of the affine projection algorithm (APA) for adaptive filtering. The APA is a natural generalization of the classical, normalized least-mean-squares (NLMS) algorithm. The book first explains how the APA evolved from the NLMS algorithm, where an affine projection view is emphasized. By looking at those adaptation algorithms from such a geometrical point of view, we can find many of the important properties of the APA, e.g., the improvement of the convergence rate over the NLMS algorithm especially for correlated input signals. After the birth of the APA in the mid-1980s, similar algorithms were put forward by other researchers independently from different perspectives. This book shows that they are variants of the APA, forming a family of APAs. Then it surveys research on the convergence behavior of the APA, where statistical analyses play important roles. It also reviews developments of techniques to reduce the computational complexity of the APA, which are important for real-time processing. It covers a recent study on the kernel APA, which extends the APA so that it is applicable to identification of not only linear systems but also nonlinear systems. The last chapter gives an overview of current topics on variable parameter APAs. The book is self-contained, and is suitable for graduate students and researchers who are interested in advanced theory of adaptive filtering.

ISBN: 9784431557388

Standard No.: 10.1007/978-4-431-55738-8doiSubjects--Topical Terms:

557949
Adaptive filters.

LC Class. No.: TK7872.F5

Dewey Class. No.: 621.3815324

Theory of affine projection algorithms for adaptive filtering
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505 0 $a Introduction -- Classical Adaptation Algorithms -- Affine Projection Algorithm -- Family of Affine Projection Algorithms -- Convergence Behavior of APA -- Reduction of Computational Complexity -- Kernel Affine Projection Algorithm -- Variable Parameter APAs -- Appendix; Matrices.
520 $a This book focuses on theoretical aspects of the affine projection algorithm (APA) for adaptive filtering. The APA is a natural generalization of the classical, normalized least-mean-squares (NLMS) algorithm. The book first explains how the APA evolved from the NLMS algorithm, where an affine projection view is emphasized. By looking at those adaptation algorithms from such a geometrical point of view, we can find many of the important properties of the APA, e.g., the improvement of the convergence rate over the NLMS algorithm especially for correlated input signals. After the birth of the APA in the mid-1980s, similar algorithms were put forward by other researchers independently from different perspectives. This book shows that they are variants of the APA, forming a family of APAs. Then it surveys research on the convergence behavior of the APA, where statistical analyses play important roles. It also reviews developments of techniques to reduce the computational complexity of the APA, which are important for real-time processing. It covers a recent study on the kernel APA, which extends the APA so that it is applicable to identification of not only linear systems but also nonlinear systems. The last chapter gives an overview of current topics on variable parameter APAs. The book is self-contained, and is suitable for graduate students and researchers who are interested in advanced theory of adaptive filtering.
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