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Article: Convergence of discrete-time recurrent neural networks with variable delay

TitleConvergence of discrete-time recurrent neural networks with variable delay
Authors
KeywordsComponentwise Exponential Stability
Discrete-Time
Global Exponential Stability
Lyapunov Functional
Recurrent Neural Networks (Rnns)
Variable Delay
Issue Date2005
PublisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijbc/ijbc.shtml
Citation
International Journal Of Bifurcation And Chaos In Applied Sciences And Engineering, 2005, v. 15 n. 2, p. 581-595 How to Cite?
AbstractIn this paper, some global exponential stability criteria for the equilibrium point of discrete-time recurrent neural networks with variable delay are presented by using the linear matrix inequality (LMI) approach. The neural networks considered are assumed to have asymmetric weighting matrices throughout this paper. On the other hand, by applying matrix decomposition, the model is embedded into a cooperative one, the latter possesses important order-preserving properties which are basic to our analysis. A sufficient condition is obtained ensuring the componentwise exponential stability of the system with specific performances such as decay rate and trajectory bounds. © World Scientific Publishing Company.
Persistent Identifierhttp://hdl.handle.net/10722/156760
ISSN
2014 Impact Factor: 1.078
2013 SCImago Journal Rankings: 0.717
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLiang, Jen_US
dc.contributor.authorCao, Jen_US
dc.contributor.authorLam, Jen_US
dc.date.accessioned2012-08-08T08:43:51Z-
dc.date.available2012-08-08T08:43:51Z-
dc.date.issued2005en_US
dc.identifier.citationInternational Journal Of Bifurcation And Chaos In Applied Sciences And Engineering, 2005, v. 15 n. 2, p. 581-595en_US
dc.identifier.issn0218-1274en_US
dc.identifier.urihttp://hdl.handle.net/10722/156760-
dc.description.abstractIn this paper, some global exponential stability criteria for the equilibrium point of discrete-time recurrent neural networks with variable delay are presented by using the linear matrix inequality (LMI) approach. The neural networks considered are assumed to have asymmetric weighting matrices throughout this paper. On the other hand, by applying matrix decomposition, the model is embedded into a cooperative one, the latter possesses important order-preserving properties which are basic to our analysis. A sufficient condition is obtained ensuring the componentwise exponential stability of the system with specific performances such as decay rate and trajectory bounds. © World Scientific Publishing Company.en_US
dc.languageengen_US
dc.publisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijbc/ijbc.shtmlen_US
dc.relation.ispartofInternational Journal of Bifurcation and Chaos in Applied Sciences and Engineeringen_US
dc.subjectComponentwise Exponential Stabilityen_US
dc.subjectDiscrete-Timeen_US
dc.subjectGlobal Exponential Stabilityen_US
dc.subjectLyapunov Functionalen_US
dc.subjectRecurrent Neural Networks (Rnns)en_US
dc.subjectVariable Delayen_US
dc.titleConvergence of discrete-time recurrent neural networks with variable delayen_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.doi10.1142/S0218127405012235en_US
dc.identifier.scopuseid_2-s2.0-18644377250en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-18644377250&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume15en_US
dc.identifier.issue2en_US
dc.identifier.spage581en_US
dc.identifier.epage595en_US
dc.identifier.isiWOS:000228906700012-
dc.publisher.placeSingaporeen_US
dc.identifier.scopusauthoridLiang, J=24544407400en_US
dc.identifier.scopusauthoridCao, J=7403354075en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US

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