Adaptive polynomial filters

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Publication Type Journal Article
School or College College of Engineering
Department Electrical & Computer Engineering
Creator Mathews, V. John
Title Adaptive polynomial filters
Date 1991
Description While linear filter are useful in a large number of applications and relatively simple from conceptual and implementational view points. there are many practical situations that require nonlinear processing of the signals involved. This article explains adaptive nonlinear filters equipped with polynomial models of nonlinearity. The polynomial systems considered are those nonlinear systems whose output signals can be related to the input signals through a truncated Volterra series expansion, or a recursive nonlinear difference equation. The Volterra series expansion can model a large class of nonlinear systems and is attractive in filtering applications because the expansion is a linear combination of nonlinear functions of the input signal. The basic ideas behind the development of gradient and recursive least-squares adaptive Volterra filters are first discussed. followed by adaptive algorithms using system models involving recursive nonlinear difference equations. Such systems are attractive because they may be able to approximate many nonlinear systems with great parsimony in the use pf coefficients. Also discussed are current research trends and new results and problem areas associated with these nonlinear filters. A lattice structure for polynomial models is also described.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Volume 8
Issue 3
First Page 10
Last Page 26
Language eng
Bibliographic Citation Mathews, V. J. (1991). Adaptive polynomial filters. IEEE Signal Processing Magazine, 8(3), 10-26. July.
Rights Management (c) 1991 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 1,482,249 bytes
Identifier ir-main,15075
ARK ark:/87278/s6pc3m3f
Setname ir_uspace
Date Created 2012-06-13
Date Modified 2021-05-06
ID 707296
Reference URL https://collections.lib.utah.edu/ark:/87278/s6pc3m3f
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