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Article: Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays

TitleStochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays
Authors
KeywordsFuzzy systems
Hopfield neural networks
Stability
Stochastic systems
Time-varying delay systems
Issue Date2005
PublisherIEEE.
Citation
Ieee Transactions On Circuits And Systems Ii: Express Briefs, 2005, v. 52 n. 5, p. 251-255 How to Cite?
AbstractThe ordinary Takagi-Sugeno (TS) fuzzy models have provided an approach to represent complex nonlinear systems to a set of linear sub-models by using fuzzy sets and fuzzy reasoning. In this paper, stochastic fuzzy Hopfield neural networks with time-varying delays (SFVDHNNs) are studied. The model of SFVDHNN is first establisbed as a modified TS fuzzy model in which the consequent parts are composed of a set of stochastic Hopfield neural networks with time-varying delays. Secondly, the global exponential stability in the mean square for SFVDHNN is studied by using the Lyapunov-Krasovskii approach. Stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages. © 2005 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/43012
ISSN
2006 Impact Factor: 0.922
2007 SCImago Journal Rankings: 1.092
References

 

DC FieldValueLanguage
dc.contributor.authorHuang, Hen_HK
dc.contributor.authorHo, DWCen_HK
dc.contributor.authorLam, Jen_HK
dc.date.accessioned2007-03-23T04:36:53Z-
dc.date.available2007-03-23T04:36:53Z-
dc.date.issued2005en_HK
dc.identifier.citationIeee Transactions On Circuits And Systems Ii: Express Briefs, 2005, v. 52 n. 5, p. 251-255en_HK
dc.identifier.issn1057-7130en_HK
dc.identifier.urihttp://hdl.handle.net/10722/43012-
dc.description.abstractThe ordinary Takagi-Sugeno (TS) fuzzy models have provided an approach to represent complex nonlinear systems to a set of linear sub-models by using fuzzy sets and fuzzy reasoning. In this paper, stochastic fuzzy Hopfield neural networks with time-varying delays (SFVDHNNs) are studied. The model of SFVDHNN is first establisbed as a modified TS fuzzy model in which the consequent parts are composed of a set of stochastic Hopfield neural networks with time-varying delays. Secondly, the global exponential stability in the mean square for SFVDHNN is studied by using the Lyapunov-Krasovskii approach. Stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages. © 2005 IEEE.en_HK
dc.format.extent169974 bytes-
dc.format.extent35328 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Circuits and Systems II: Express Briefsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2005 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.subjectFuzzy systemsen_HK
dc.subjectHopfield neural networksen_HK
dc.subjectStabilityen_HK
dc.subjectStochastic systemsen_HK
dc.subjectTime-varying delay systemsen_HK
dc.titleStochastic stability analysis of fuzzy Hopfield neural networks with time-varying delaysen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1549-7747&volume=52&issue=5&spage=251&epage=255&date=2005&atitle=Stochastic+stability+analysis+of+fuzzy+hopfield+neural+networks+with+time-varying+delaysen_HK
dc.identifier.emailLam, J:james.lam@hku.hken_HK
dc.identifier.authorityLam, J=rp00133en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TCSII.2005.846305en_HK
dc.identifier.scopuseid_2-s2.0-20444396501en_HK
dc.identifier.hkuros102669-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-20444396501&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume52en_HK
dc.identifier.issue5en_HK
dc.identifier.spage251en_HK
dc.identifier.epage255en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridHuang, H=34770542600en_HK
dc.identifier.scopusauthoridHo, DWC=7402971938en_HK
dc.identifier.scopusauthoridLam, J=7201973414en_HK

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