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Conference Paper: Self-tuning neurofuzzy control for nonlinear systems with offset

TitleSelf-tuning neurofuzzy control for nonlinear systems with offset
Authors
KeywordsIntegrating controllers
Neurofuzzy networks
Nonlinear controllers
Self-tuning controllers
Issue Date2001
PublisherIEEE.
Citation
Joint the 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, BC, Canada, 25-28 July 2001, v. 2, p. 1021-1026 How to Cite?
AbstractA self-tuning neurofuzzy controller with an ability to remove offsets is derived in this paper based on the self-tuning integrating controller derived for the local linear model. The training target for the proposed controllers is derived, and they can be trained by the simplified recursive least squares (RLS) method with a computing time that is linear instead of geometric in the number of weights in the network. Further, the simplified RLS method not only has the same convergence property as the RLS method, it also has a better ability in tracking varying parameters. The performance of the self-tuning neurofuzzy controller is illustrated by examples involving both linear and nonlinear systems.
Persistent Identifierhttp://hdl.handle.net/10722/46665
References

 

DC FieldValueLanguage
dc.contributor.authorChan, CWen_HK
dc.contributor.authorLiu, XJen_HK
dc.contributor.authorYeung, WKen_HK
dc.date.accessioned2007-10-30T06:55:26Z-
dc.date.available2007-10-30T06:55:26Z-
dc.date.issued2001en_HK
dc.identifier.citationJoint the 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, BC, Canada, 25-28 July 2001, v. 2, p. 1021-1026en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46665-
dc.description.abstractA self-tuning neurofuzzy controller with an ability to remove offsets is derived in this paper based on the self-tuning integrating controller derived for the local linear model. The training target for the proposed controllers is derived, and they can be trained by the simplified recursive least squares (RLS) method with a computing time that is linear instead of geometric in the number of weights in the network. Further, the simplified RLS method not only has the same convergence property as the RLS method, it also has a better ability in tracking varying parameters. The performance of the self-tuning neurofuzzy controller is illustrated by examples involving both linear and nonlinear systems.en_HK
dc.format.extent517045 bytes-
dc.format.extent5145 bytes-
dc.format.mimetypeapplication/pdf-
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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
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.subjectIntegrating controllersen_HK
dc.subjectNeurofuzzy networksen_HK
dc.subjectNonlinear controllersen_HK
dc.subjectSelf-tuning controllersen_HK
dc.titleSelf-tuning neurofuzzy control for nonlinear systems with offseten_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChan, CW: mechan@hkucc.hku.hken_HK
dc.identifier.authorityChan, CW=rp00088en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/NAFIPS.2001.944745en_HK
dc.identifier.scopuseid_2-s2.0-0035792272en_HK
dc.identifier.hkuros68090-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035792272&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume2en_HK
dc.identifier.spage1021en_HK
dc.identifier.epage1026en_HK
dc.identifier.scopusauthoridChan, CW=7404814060en_HK
dc.identifier.scopusauthoridLiu, XJ=7409294512en_HK
dc.identifier.scopusauthoridYeung, WK=24345897100en_HK

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