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Conference Paper: Support vector recurrent neurofuzzy networks in modeling nonlinear systems with correlated noise

TitleSupport vector recurrent neurofuzzy networks in modeling nonlinear systems with correlated noise
Authors
KeywordsCorrelated noise
Recurrent neurofuzzy network
Sensitivity model
Support vectors
Issue Date2001
PublisherIEEE.
Citation
Annual Conference Of The North American Fuzzy Information Processing Society - Nafips, 2001, v. 1, p. 545-550 How to Cite?
AbstractGood generalization results are obtained from neurofuzzy networks if its structure is suitably chosen. To select the structure of neurofuzzy networks, the authors proposed a construction algorithm that is derived from the Support Vector Regression. However, the modeling errors are assumed to be uncorrelated. In this paper, systems with correlated modeling errors are considered. The correlated noise is modeled separately by a recurrent network. The overall network is referred to as the support vector recurrent neurofuzzy networks. The prediction error method is used to train the networks, where the derivatives are computed by a sensitivity model. The performance of proposed networks is illustrated by an example involving a nonlinear dynamic system corrupted by correlated noise.
Persistent Identifierhttp://hdl.handle.net/10722/46664
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.accessioned2007-10-30T06:55:25Z-
dc.date.available2007-10-30T06:55:25Z-
dc.date.issued2001en_HK
dc.identifier.citationAnnual Conference Of The North American Fuzzy Information Processing Society - Nafips, 2001, v. 1, p. 545-550en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46664-
dc.description.abstractGood generalization results are obtained from neurofuzzy networks if its structure is suitably chosen. To select the structure of neurofuzzy networks, the authors proposed a construction algorithm that is derived from the Support Vector Regression. However, the modeling errors are assumed to be uncorrelated. In this paper, systems with correlated modeling errors are considered. The correlated noise is modeled separately by a recurrent network. The overall network is referred to as the support vector recurrent neurofuzzy networks. The prediction error method is used to train the networks, where the derivatives are computed by a sensitivity model. The performance of proposed networks is illustrated by an example involving a nonlinear dynamic system corrupted by correlated noise.en_HK
dc.format.extent415102 bytes-
dc.format.extent5145 bytes-
dc.format.extent3469 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPSen_HK
dc.rights©2001 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.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectCorrelated noiseen_HK
dc.subjectRecurrent neurofuzzy networken_HK
dc.subjectSensitivity modelen_HK
dc.subjectSupport vectorsen_HK
dc.titleSupport vector recurrent neurofuzzy networks in modeling nonlinear systems with correlated noiseen_HK
dc.typeConference_Paperen_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.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/NAFIPS.2001.944311en_HK
dc.identifier.scopuseid_2-s2.0-0035792598en_HK
dc.identifier.hkuros68089-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035792598&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume1en_HK
dc.identifier.spage545en_HK
dc.identifier.epage550en_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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