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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2015

01.12.2015 | Original Article

Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization

verfasst von: Shuen Wang, Zhenzhen Han, Fucai Liu, Yinggan Tang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2015

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Abstract

In this paper, we present a method for nonlinear system identification. The proposed method adopts least squares support vector machine (LSSVM) to approximate a nonlinear autoregressive model with eXogeneous (NARX). First, the orders of NARX model are determined from input–output data via Lipschitz quotient criterion. Then, an LSSVM model is used to approximate the NARX model. To obtain an efficient LSSVM model, a novel particle swarm optimization with adaptive inertia weight is proposed to tune the hyper-parameters of LSSVM. Two experimental results are given to illustrate the effectiveness of the proposed method.
Vorheriger Artikel ART: group recommendation approaches for automatically detected groups
Nächster Artikel A matching model of mathematical expressions with FDS based index

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Metadaten
Titel
Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization
verfasst von
Shuen Wang
Zhenzhen Han
Fucai Liu
Yinggan Tang
Publikationsdatum
01.12.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0403-0

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