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Article: Novel global robust stability criteria for interval neural networks with multiple time-varying delays

TitleNovel global robust stability criteria for interval neural networks with multiple time-varying delays
Authors
KeywordsGlobal Asymptotic Stability
Interval Systems
Linear Matrix Inequality
Neural Networks
Time-Varying Delays
Issue Date2005
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/physleta
Citation
Physics Letters, Section A: General, Atomic And Solid State Physics, 2005, v. 342 n. 4, p. 322-330 How to Cite?
AbstractThis Letter is concerned with the problem of robust stability analysis for interval neural networks with multiple time-varying delays and parameter uncertainties. The parameter uncertainties are assumed to be bounded in given compact sets and the activation functions are supposed to be bounded and globally Lipschitz continuous. A sufficient condition is obtained by means of Lyapunov functionals, which guarantees the existence, uniqueness and global asymptotic stability of the delayed neural network for all admissible uncertainties. This condition is in terms of a linear matrix inequality (LMI), which can be easily checked by using recently developed algorithms in solving LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method. © 2005 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/156767
ISSN
2015 Impact Factor: 1.677
2015 SCImago Journal Rankings: 0.755
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorXu, Sen_US
dc.contributor.authorLam, Jen_US
dc.contributor.authorHo, DWCen_US
dc.date.accessioned2012-08-08T08:43:53Z-
dc.date.available2012-08-08T08:43:53Z-
dc.date.issued2005en_US
dc.identifier.citationPhysics Letters, Section A: General, Atomic And Solid State Physics, 2005, v. 342 n. 4, p. 322-330en_US
dc.identifier.issn0375-9601en_US
dc.identifier.urihttp://hdl.handle.net/10722/156767-
dc.description.abstractThis Letter is concerned with the problem of robust stability analysis for interval neural networks with multiple time-varying delays and parameter uncertainties. The parameter uncertainties are assumed to be bounded in given compact sets and the activation functions are supposed to be bounded and globally Lipschitz continuous. A sufficient condition is obtained by means of Lyapunov functionals, which guarantees the existence, uniqueness and global asymptotic stability of the delayed neural network for all admissible uncertainties. This condition is in terms of a linear matrix inequality (LMI), which can be easily checked by using recently developed algorithms in solving LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method. © 2005 Elsevier B.V. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/physletaen_US
dc.relation.ispartofPhysics Letters, Section A: General, Atomic and Solid State Physicsen_US
dc.subjectGlobal Asymptotic Stabilityen_US
dc.subjectInterval Systemsen_US
dc.subjectLinear Matrix Inequalityen_US
dc.subjectNeural Networksen_US
dc.subjectTime-Varying Delaysen_US
dc.titleNovel global robust stability criteria for interval neural networks with multiple time-varying delaysen_US
dc.typeArticleen_US
dc.identifier.emailLam, J:james.lam@hku.hken_US
dc.identifier.authorityLam, J=rp00133en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.physleta.2005.05.016en_US
dc.identifier.scopuseid_2-s2.0-21244505313en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-21244505313&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume342en_US
dc.identifier.issue4en_US
dc.identifier.spage322en_US
dc.identifier.epage330en_US
dc.identifier.isiWOS:000230443000008-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridXu, S=7404438591en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US
dc.identifier.scopusauthoridHo, DWC=7402971938en_US

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