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Adaptive Filtering: Algorithms and Practical Implementation
Kategorie	Beschreibung
036a	XA-DE
037b	eng
087q	978-3-030-29056-6
100	Diniz, Paulo S. R. ¬[VerfasserIn]¬
331	Adaptive Filtering
335	Algorithms and Practical Implementation
403	5th ed. 2020
410	Cham
412	Springer
425	2020
425a	2020
433	1 Online-Ressource (XVIII, 495 p. 232 illus., 23 illus. in color)
451b	Springer eBooks. Engineering
527	Erscheint auch als (Druck-Ausgabe)ISBN: 978-3-030-29056-6
540a	ISBN 978-3-030-29057-3
700	\|TTBM
700	\|UYS
700	\|TTBM
700	\|TEC008000
700	\|*93-02
700	\|93E11
700	\|60G35
700	\|62M20
700	\|62P30
700	\|93E10
700	\|93E12
700	\|93C40
700	\|93C55
700b	\|621.382
700c	\|TK5102.9
700c	\|TA1637-1638
750	Introduction to Adaptive Filtering -- Fundamentals of Adaptive Filtering -- The Least-Mean-Square (LMS) Algorithm -- LMS-Based Algorithms -- LMS-Based Algorithms -- Conventional RLS Adaptive Filter -- Set-Membership Adaptive Filtering -- Adaptive Lattice-Based RLS Algorithms -- Fast Transversal RLS Algorithms -- QR-Decomposition-Based RLS Filters -- Adaptive IIR Filters -- Nonlinear Adaptive Filtering -- Subband Adaptive Filters -- Blind Adaptive Filtering -- Kalman Filtering -- Complex Differentiation -- Quantization Effects in the LMS Algorithm -- Quantization Effects in the RLS Algorithm -- Analysis of Set-Membership Affine Projection Algorithm -- Index
753	In the fifth edition of this textbook, author Paulo S.R. Diniz presents updated text on the basic concepts of adaptive signal processing and adaptive filtering. He first introduces the main classes of adaptive filtering algorithms in a unified framework, using clear notations that facilitate actual implementation. Algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Examples address up-to-date problems drawn from actual applications. Several chapters are expanded and a new chapter ‘Kalman Filtering’ is included. The book provides a concise background on adaptive filtering, including the family of LMS, affine projection, RLS, set-membership algorithms and Kalman filters, as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Problems are included at the end of chapters. A MATLAB package is provided so the reader can solve new problems and test algorithms. The book also offers easy access to working algorithms for practicing engineers
012	168496721X
081	Adaptive Filtering
100	Springer E-Book
125a	Elektronischer Volltext - Campuslizenz
655e	$uhttps://doi.org/10.1007/978-3-030-29057-3