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Article: Exponential stability of high-order bidirectional associative memory neural networks with time delays

TitleExponential stability of high-order bidirectional associative memory neural networks with time delays
Authors
Cao, JLiang, JLam, J
KeywordsBidirectional Associative Memory (Bam)
Exponential Stability
High-Order Neural Networks
Linear Matrix Inequality
Lyapunov Functional
Time Delays
Issue Date2004
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/physd
Citation
Physica D: Nonlinear Phenomena, 2004, v. 199 n. 3-4, p. 425-436 How to Cite?
DOI: http://dx.doi.org/10.1016/j.physd.2004.09.012
AbstractIn this paper, exponential stability is studied for a class of high-order bidirectional associative memory (BAM) neural networks with time delays. By employing the linear matrix inequality (LMI) and the Lyapunov functional methods, several sufficient conditions are obtained for ensuring the system to be globally exponentially stable. Two illustrative examples are also given in the end of this paper to show the effectiveness of our results. © 2004 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/156723
ISSN
0167-2789
2021 Impact Factor: 3.751
2020 SCImago Journal Rankings: 1.016
ISI Accession Number ID
WOS:000226128200008
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorCao, Jen_US
dc.contributor.authorLiang, Jen_US
dc.contributor.authorLam, Jen_US
dc.date.accessioned2012-08-08T08:43:41Z-
dc.date.available2012-08-08T08:43:41Z-
dc.date.issued2004en_US
dc.identifier.citationPhysica D: Nonlinear Phenomena, 2004, v. 199 n. 3-4, p. 425-436en_US
dc.identifier.issn0167-2789en_US
dc.identifier.urihttp://hdl.handle.net/10722/156723-
dc.description.abstractIn this paper, exponential stability is studied for a class of high-order bidirectional associative memory (BAM) neural networks with time delays. By employing the linear matrix inequality (LMI) and the Lyapunov functional methods, several sufficient conditions are obtained for ensuring the system to be globally exponentially stable. Two illustrative examples are also given in the end of this paper to show the effectiveness of our results. © 2004 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/physden_US
dc.relation.ispartofPhysica D: Nonlinear Phenomenaen_US
dc.subjectBidirectional Associative Memory (Bam)en_US
dc.subjectExponential Stabilityen_US
dc.subjectHigh-Order Neural Networksen_US
dc.subjectLinear Matrix Inequalityen_US
dc.subjectLyapunov Functionalen_US
dc.subjectTime Delaysen_US
dc.titleExponential stability of high-order bidirectional associative memory neural networks with time 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.physd.2004.09.012en_US
dc.identifier.scopuseid_2-s2.0-10644236158en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-10644236158&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume199en_US
dc.identifier.issue3-4en_US
dc.identifier.spage425en_US
dc.identifier.epage436en_US
dc.identifier.isiWOS:000226128200008-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridCao, J=7403354075en_US
dc.identifier.scopusauthoridLiang, J=24544407400en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US
dc.identifier.issnl0167-2789-

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