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Adaptive learning methods for nonlin...

Comminiello, Danilo,

Adaptive learning methods for nonlinear system modeling /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Adaptive learning methods for nonlinear system modeling // edited by Danilo Comminiello, José C. Principe.
other author:	Comminiello, Danilo,
Description:	1 online resource
Subject:	Adaptive signal processing. -
Online resource:	https://www.sciencedirect.com/science/book/9780128129760
ISBN:	9780128129777

Adaptive learning methods for nonlinear system modeling /
Adaptive learning methods for nonlinear system modeling /edited by Danilo Comminiello, José C. Principe. - 1 online resource

Includes bibliographical references and index.

Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems.

ISBN: 9780128129777

Nat. Bib. No.: GBB8A3493bnbSubjects--Topical Terms:

557333
Adaptive signal processing.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.382/2

Adaptive learning methods for nonlinear system modeling /
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082 0 4 $a 621.382/2 $2 23
245 0 0 $a Adaptive learning methods for nonlinear system modeling / $c edited by Danilo Comminiello, José C. Principe.
264 1 $a Kidlington, Oxford, United Kingdom : $b Butterworth-Heinemann, an imprint of Elsevier, $c 2018.
300 $a 1 online resource
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
504 $a Includes bibliographical references and index.
520 $a Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems.
650 0 $a Adaptive signal processing. $3 557333
655 4 $a Electronic books. $2 local $3 554714
700 1 $a Comminiello, Danilo, $e editor. $3 1295125
700 1 $a Príncipe, J. C. $q (José C.) $3 1027129
856 4 0 $u https://www.sciencedirect.com/science/book/9780128129760

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