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Article: Convergence of discrete-time recurrent neural networks with variable delay
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TitleConvergence of discrete-time recurrent neural networks with variable delay
 
AuthorsLiang, J2
Cao, J2
Lam, J1
 
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
 
CitationInternational Journal Of Bifurcation And Chaos In Applied Sciences And Engineering, 2005, v. 15 n. 2, p. 581-595 [How to Cite?]
DOI: http://dx.doi.org/10.1142/S0218127405012235
 
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.
 
ISSN0218-1274
2013 Impact Factor: 1.017
2013 SCImago Journal Rankings: 0.717
 
DOIhttp://dx.doi.org/10.1142/S0218127405012235
 
ISI Accession Number IDWOS:000228906700012
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorLiang, J
 
dc.contributor.authorCao, J
 
dc.contributor.authorLam, J
 
dc.date.accessioned2012-08-08T08:43:51Z
 
dc.date.available2012-08-08T08:43:51Z
 
dc.date.issued2005
 
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.
 
dc.description.naturelink_to_subscribed_fulltext
 
dc.identifier.citationInternational Journal Of Bifurcation And Chaos In Applied Sciences And Engineering, 2005, v. 15 n. 2, p. 581-595 [How to Cite?]
DOI: http://dx.doi.org/10.1142/S0218127405012235
 
dc.identifier.doihttp://dx.doi.org/10.1142/S0218127405012235
 
dc.identifier.epage595
 
dc.identifier.isiWOS:000228906700012
 
dc.identifier.issn0218-1274
2013 Impact Factor: 1.017
2013 SCImago Journal Rankings: 0.717
 
dc.identifier.issue2
 
dc.identifier.scopuseid_2-s2.0-18644377250
 
dc.identifier.spage581
 
dc.identifier.urihttp://hdl.handle.net/10722/156760
 
dc.identifier.volume15
 
dc.languageeng
 
dc.publisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijbc/ijbc.shtml
 
dc.publisher.placeSingapore
 
dc.relation.ispartofInternational Journal of Bifurcation and Chaos in Applied Sciences and Engineering
 
dc.relation.referencesReferences in Scopus
 
dc.subjectComponentwise Exponential Stability
 
dc.subjectDiscrete-Time
 
dc.subjectGlobal Exponential Stability
 
dc.subjectLyapunov Functional
 
dc.subjectRecurrent Neural Networks (Rnns)
 
dc.subjectVariable Delay
 
dc.titleConvergence of discrete-time recurrent neural networks with variable delay
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong
  2. Southeast University