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Article: Stability and dissipativity analysis of static neural networks with time delay

TitleStability and dissipativity analysis of static neural networks with time delay
Authors
KeywordsDissipativity
Stability
Static neural networks
Time delay
Issue Date2012
PublisherIEEE.
Citation
IEEE Transactions on Neural Networks, 2012, v. 23 n. 2, p. 199-210 How to Cite?
Abstract
This paper is concerned with the problems of stability and dissipativity analysis for static neural networks (NNs) with time delay. Some improved delay-dependent stability criteria are established for static NNs with time-varying or time-invariant delay using the delay partitioning technique. Based on these criteria, several delay-dependent sufficient conditions are given to guarantee the dissipativity of static NNs with time delay. All the given results in this paper are not only dependent upon the time delay but also upon the number of delay partitions. Some examples are given to illustrate the effectiveness and reduced conservatism of the proposed results.
Persistent Identifierhttp://hdl.handle.net/10722/164241
ISSN
2013 SCImago Journal Rankings: 1.663
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, ZGen_US
dc.contributor.authorLam, Jen_US
dc.contributor.authorSu, Hen_US
dc.contributor.authorChu, Jen_US
dc.date.accessioned2012-09-20T07:56:48Z-
dc.date.available2012-09-20T07:56:48Z-
dc.date.issued2012en_US
dc.identifier.citationIEEE Transactions on Neural Networks, 2012, v. 23 n. 2, p. 199-210en_US
dc.identifier.issn1045-9227-
dc.identifier.urihttp://hdl.handle.net/10722/164241-
dc.description.abstractThis paper is concerned with the problems of stability and dissipativity analysis for static neural networks (NNs) with time delay. Some improved delay-dependent stability criteria are established for static NNs with time-varying or time-invariant delay using the delay partitioning technique. Based on these criteria, several delay-dependent sufficient conditions are given to guarantee the dissipativity of static NNs with time delay. All the given results in this paper are not only dependent upon the time delay but also upon the number of delay partitions. Some examples are given to illustrate the effectiveness and reduced conservatism of the proposed results.-
dc.languageengen_US
dc.publisherIEEE.-
dc.relation.ispartofIEEE Transactions on Neural Networksen_US
dc.rightsIEEE Transactions on Neural Networks. Copyright © IEEE.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectDissipativity-
dc.subjectStability-
dc.subjectStatic neural networks-
dc.subjectTime delay-
dc.titleStability and dissipativity analysis of static neural networks with time delayen_US
dc.typeArticleen_US
dc.identifier.emailLam, J: james.lam@hku.hken_US
dc.identifier.authorityLam, J=rp00133en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TNNLS.2011.2178563-
dc.identifier.hkuros208800en_US
dc.identifier.volume23en_US
dc.identifier.issue2en_US
dc.identifier.spage199en_US
dc.identifier.epage210en_US
dc.identifier.isiWOS:000302704300002-
dc.publisher.placeUnited States-

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