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Article: Structure selection of neurofuzzy networks based on support vector regression

TitleStructure selection of neurofuzzy networks based on support vector regression
Authors
Issue Date2002
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207721.asp
Citation
International Journal Of Systems Science, 2002, v. 33 n. 9, p. 715-722 How to Cite?
AbstractNeurofuzzy networks are being used increasingly to model non-linear dynamic systems, since they have the approximating ability of neural networks and the transparency of fuzzy systems. However, good generalization results can only be obtained if the structure of the network is suitably chosen. It is shown here that the structure of neurofuzzy networks with scatter partitioning can be obtained from the support vectors (SV) of the Support Vector Regression (SVR), since the SVR can be transformed to a neurofuzzy network. The main advantage of this approach is that the structure of the neurofuzzy networks can now be objectively chosen, as the SV are obtained by constrained optimization for a given modelling error bound. Since neurofuzzy networks are linear-in-weights networks, the estimate of the weights of the networks can be obtained by the linear least-squares method. The properties of neurofuzzy networks based on the SV are derived, and its performance is illustrated by a simulation example involving a non-linear system, and the modeling of Southern Oscillation Index.
Persistent Identifierhttp://hdl.handle.net/10722/75498
ISSN
2015 Impact Factor: 1.947
2015 SCImago Journal Rankings: 1.083
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, CWen_HK
dc.contributor.authorChan, WCen_HK
dc.contributor.authorJayawardena, AWen_HK
dc.contributor.authorHarris, CJen_HK
dc.date.accessioned2010-09-06T07:11:44Z-
dc.date.available2010-09-06T07:11:44Z-
dc.date.issued2002en_HK
dc.identifier.citationInternational Journal Of Systems Science, 2002, v. 33 n. 9, p. 715-722en_HK
dc.identifier.issn0020-7721en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75498-
dc.description.abstractNeurofuzzy networks are being used increasingly to model non-linear dynamic systems, since they have the approximating ability of neural networks and the transparency of fuzzy systems. However, good generalization results can only be obtained if the structure of the network is suitably chosen. It is shown here that the structure of neurofuzzy networks with scatter partitioning can be obtained from the support vectors (SV) of the Support Vector Regression (SVR), since the SVR can be transformed to a neurofuzzy network. The main advantage of this approach is that the structure of the neurofuzzy networks can now be objectively chosen, as the SV are obtained by constrained optimization for a given modelling error bound. Since neurofuzzy networks are linear-in-weights networks, the estimate of the weights of the networks can be obtained by the linear least-squares method. The properties of neurofuzzy networks based on the SV are derived, and its performance is illustrated by a simulation example involving a non-linear system, and the modeling of Southern Oscillation Index.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207721.aspen_HK
dc.relation.ispartofInternational Journal of Systems Scienceen_HK
dc.titleStructure selection of neurofuzzy networks based on support vector regressionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0020-7721&volume=33&issue=9&spage=715&epage=722&date=2002&atitle=Structure+selection+of+neurofuzzy+networks+based+on+support+vector+regressionen_HK
dc.identifier.emailChan, CW: mechan@hkucc.hku.hken_HK
dc.identifier.authorityChan, CW=rp00088en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/00207720210147089en_HK
dc.identifier.scopuseid_2-s2.0-0037063250en_HK
dc.identifier.hkuros76305en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0037063250&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume33en_HK
dc.identifier.issue9en_HK
dc.identifier.spage715en_HK
dc.identifier.epage722en_HK
dc.identifier.isiWOS:000178500100002-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChan, CW=7404814060en_HK
dc.identifier.scopusauthoridChan, WC=36503653500en_HK
dc.identifier.scopusauthoridJayawardena, AW=7005049253en_HK
dc.identifier.scopusauthoridHarris, CJ=7403875034en_HK

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