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Article: On the modelling of nonlinear dynamic systems using support vector neural networks

TitleOn the modelling of nonlinear dynamic systems using support vector neural networks
Authors
Issue Date2001
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappai
Citation
Engineering Applications Of Artificial Intelligence, 2001, v. 14 n. 2, p. 105-113 How to Cite?
AbstractThough neural networks have the ability to approximate nonlinear functions with arbitrary accuracy, good generalization results are obtained only if the structure of the network is suitably chosen. Therefore, selecting the `best' structure of the neural networks is an important problem. Support vector neural networks (SVNN) are proposed in this paper, which can provide a solution to this problem. The structure of the SVNN is obtained by a constrained minimization for a given error bound similar to that in the support vector regression (SVR). After the structure is selected, its weights are computed by the linear least squares method, as it is a linear-in-weight network. Consequently, in contrast to the SVR, the output of the SVNN is unbiased. It is further shown here that the variance of the modelling error of the SVNN is bounded by the square of the given error bound in selecting its structure, and is smaller than that of the SVR. The performance of the SVNN is illustrated by a simulation example involving a benchmark nonlinear system.
Persistent Identifierhttp://hdl.handle.net/10722/76101
ISSN
2015 Impact Factor: 2.368
2015 SCImago Journal Rankings: 1.371
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, WCen_HK
dc.contributor.authorChan, CWen_HK
dc.contributor.authorCheung, KCen_HK
dc.contributor.authorHarris, CJen_HK
dc.date.accessioned2010-09-06T07:17:39Z-
dc.date.available2010-09-06T07:17:39Z-
dc.date.issued2001en_HK
dc.identifier.citationEngineering Applications Of Artificial Intelligence, 2001, v. 14 n. 2, p. 105-113en_HK
dc.identifier.issn0952-1976en_HK
dc.identifier.urihttp://hdl.handle.net/10722/76101-
dc.description.abstractThough neural networks have the ability to approximate nonlinear functions with arbitrary accuracy, good generalization results are obtained only if the structure of the network is suitably chosen. Therefore, selecting the `best' structure of the neural networks is an important problem. Support vector neural networks (SVNN) are proposed in this paper, which can provide a solution to this problem. The structure of the SVNN is obtained by a constrained minimization for a given error bound similar to that in the support vector regression (SVR). After the structure is selected, its weights are computed by the linear least squares method, as it is a linear-in-weight network. Consequently, in contrast to the SVR, the output of the SVNN is unbiased. It is further shown here that the variance of the modelling error of the SVNN is bounded by the square of the given error bound in selecting its structure, and is smaller than that of the SVR. The performance of the SVNN is illustrated by a simulation example involving a benchmark nonlinear system.en_HK
dc.languageengen_HK
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappaien_HK
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_HK
dc.titleOn the modelling of nonlinear dynamic systems using support vector neural networksen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0952-1976&volume=14&spage=105&epage=113&date=2001&atitle=On+the+modelling+of+nonlinear+dynamic+systems+using+support+vector+neural+networksen_HK
dc.identifier.emailChan, CW: mechan@hkucc.hku.hken_HK
dc.identifier.emailCheung, KC: kccheung@hkucc.hku.hken_HK
dc.identifier.authorityChan, CW=rp00088en_HK
dc.identifier.authorityCheung, KC=rp01322en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/S0952-1976(00)00069-5en_HK
dc.identifier.scopuseid_2-s2.0-0035311654en_HK
dc.identifier.hkuros59083en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035311654&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume14en_HK
dc.identifier.issue2en_HK
dc.identifier.spage105en_HK
dc.identifier.epage113en_HK
dc.identifier.isiWOS:000167954100001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChan, WC=36503653500en_HK
dc.identifier.scopusauthoridChan, CW=7404814060en_HK
dc.identifier.scopusauthoridCheung, KC=7402406698en_HK
dc.identifier.scopusauthoridHarris, CJ=7403875034en_HK

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