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Article: Global exponential stability of impulsive high-order BAM neural networks with time-varying delays

TitleGlobal exponential stability of impulsive high-order BAM neural networks with time-varying delays
Authors
Ho, DWCLiang, JLam, J
KeywordsBidirectional associative memory neural networks
Delay
Differential inequality
Global exponential stability
Impulse
Linear matrix inequality
Issue Date2006
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/neunet
Citation
Neural Networks, 2006, v. 19 n. 10, p. 1581-1590 How to Cite?
DOI: http://dx.doi.org/10.1016/j.neunet.2006.02.006
AbstractIn this paper, global exponential stability and exponential convergence are studied for a class of impulsive high-order bidirectional associative memory (BAM) neural networks with time-varying delays. By employing linear matrix inequalities (LMIs) and differential inequalities with delays and impulses, several sufficient conditions are obtained for ensuring the system to be globally exponentially stable. Three illustrative examples are also given at the end of this paper to show the effectiveness of our results. © 2006 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/156862
ISSN
0893-6080
2023 Impact Factor: 6.0
2023 SCImago Journal Rankings: 2.605
ISI Accession Number ID
WOS:000243215200012
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorHo, DWCen_US
dc.contributor.authorLiang, Jen_US
dc.contributor.authorLam, Jen_US
dc.date.accessioned2012-08-08T08:44:19Z-
dc.date.available2012-08-08T08:44:19Z-
dc.date.issued2006en_US
dc.identifier.citationNeural Networks, 2006, v. 19 n. 10, p. 1581-1590en_US
dc.identifier.issn0893-6080en_US
dc.identifier.urihttp://hdl.handle.net/10722/156862-
dc.description.abstractIn this paper, global exponential stability and exponential convergence are studied for a class of impulsive high-order bidirectional associative memory (BAM) neural networks with time-varying delays. By employing linear matrix inequalities (LMIs) and differential inequalities with delays and impulses, several sufficient conditions are obtained for ensuring the system to be globally exponentially stable. Three illustrative examples are also given at the end of this paper to show the effectiveness of our results. © 2006 Elsevier Ltd. All rights reserved.en_US
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/neuneten_US
dc.relation.ispartofNeural Networksen_US
dc.subjectBidirectional associative memory neural networks-
dc.subjectDelay-
dc.subjectDifferential inequality-
dc.subjectGlobal exponential stability-
dc.subjectImpulse-
dc.subjectLinear matrix inequality-
dc.subject.meshAlgorithmsen_US
dc.subject.meshHumansen_US
dc.subject.meshLinear Modelsen_US
dc.subject.meshMemory - Physiologyen_US
dc.subject.meshNerve Neten_US
dc.subject.meshNeural Networks (Computer)en_US
dc.subject.meshNumerical Analysis, Computer-Assisteden_US
dc.subject.meshTime Factorsen_US
dc.titleGlobal exponential stability of impulsive high-order BAM neural networks with 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.neunet.2006.02.006en_US
dc.identifier.pmid16580174-
dc.identifier.scopuseid_2-s2.0-33751202426en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33751202426&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume19en_US
dc.identifier.issue10en_US
dc.identifier.spage1581en_US
dc.identifier.epage1590en_US
dc.identifier.isiWOS:000243215200012-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridHo, DWC=7402971938en_US
dc.identifier.scopusauthoridLiang, J=24544407400en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US
dc.identifier.citeulike3860742-
dc.identifier.issnl0893-6080-

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