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010   $a4-431-55738-5
024 7 $a10.1007/978-4-431-55738-8
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100   $a20150722d2016      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aTheory of Affine Projection Algorithms for Adaptive Filtering /$fby Kazuhiko Ozeki
205   $a1st ed. 2016.
210  1$aTokyo :$cSpringer Japan :$cImprint: Springer,$d2016.
215   $a1 online resource (229 p.)
225 1 $aMathematics for Industry,$x2198-350X ;$v22
300   $aDescription based upon print version of record.
311   $a4-431-55737-7 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- 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.
330   $aThis book focuses on theoretical aspects of the affine projection algorithm (APA) for adaptive filtering. The APA is a natural generalization of the classical, normalized least-mean-squares (NLMS) algorithm. The book first explains how the APA evolved from the NLMS algorithm, where an affine projection view is emphasized. By looking at those adaptation algorithms from such a geometrical point of view, we can find many of the important properties of the APA, e.g., the improvement of the convergence rate over the NLMS algorithm especially for correlated input signals. After the birth of the APA in the mid-1980s, similar algorithms were put forward by other researchers independently from different perspectives. This book shows that they are variants of the APA, forming a family of APAs. Then it surveys research on the convergence behavior of the APA, where statistical analyses play important roles. It also reviews developments of techniques to reduce the computational complexity of the APA, which are important for real-time processing. It covers a recent study on the kernel APA, which extends the APA so that it is applicable to identification of not only linear systems but also nonlinear systems. The last chapter gives an overview of current topics on variable parameter APAs. The book is self-contained, and is suitable for graduate students and researchers who are interested in advanced theory of adaptive filtering.
410  0$aMathematics for Industry,$x2198-350X ;$v22
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aMathematical models
606   $aApplied mathematics
606   $aEngineering mathematics
606   $aGeometry, Projective
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
606   $aMathematical Modeling and Industrial Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M14068
606   $aMathematical and Computational Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11006
606   $aProjective Geometry$3https://scigraph.springernature.com/ontologies/product-market-codes/M21050
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615  0$aMathematical models.
615  0$aApplied mathematics.
615  0$aEngineering mathematics.
615  0$aGeometry, Projective.
615 14$aSignal, Image and Speech Processing.
615 24$aMathematical Modeling and Industrial Mathematics.
615 24$aMathematical and Computational Engineering.
615 24$aProjective Geometry.
676   $a620
700   $aOzeki$b Kazuhiko$4aut$4http://id.loc.gov/vocabulary/relators/aut$0764103
906   $aBOOK
912   $a9910254177803321
996   $aTheory of Affine Projection Algorithms for Adaptive Filtering$91551061
997   $aUNINA