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Article: Stability and dissipativity analysis of static neural networks with time delay
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TitleStability and dissipativity analysis of static neural networks with time delay
 
AuthorsWu, ZG
Lam, J
Su, H
Chu, J
 
KeywordsDissipativity
Stability
Static neural networks
Time delay
 
Issue Date2012
 
PublisherIEEE.
 
CitationIEEE Transactions on Neural Networks, 2012, v. 23 n. 2, p. 199-210 [How to Cite?]
DOI: http://dx.doi.org/10.1109/TNNLS.2011.2178563
 
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.
 
ISSN1045-9227
2012 SCImago Journal Rankings: 1.480
 
DOIhttp://dx.doi.org/10.1109/TNNLS.2011.2178563
 
ISI Accession Number IDWOS:000302704300002
 
DC FieldValue
dc.contributor.authorWu, ZG
 
dc.contributor.authorLam, J
 
dc.contributor.authorSu, H
 
dc.contributor.authorChu, J
 
dc.date.accessioned2012-09-20T07:56:48Z
 
dc.date.available2012-09-20T07:56:48Z
 
dc.date.issued2012
 
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.description.naturepublished_or_final_version
 
dc.identifier.citationIEEE Transactions on Neural Networks, 2012, v. 23 n. 2, p. 199-210 [How to Cite?]
DOI: http://dx.doi.org/10.1109/TNNLS.2011.2178563
 
dc.identifier.doihttp://dx.doi.org/10.1109/TNNLS.2011.2178563
 
dc.identifier.epage210
 
dc.identifier.hkuros208800
 
dc.identifier.isiWOS:000302704300002
 
dc.identifier.issn1045-9227
2012 SCImago Journal Rankings: 1.480
 
dc.identifier.issue2
 
dc.identifier.spage199
 
dc.identifier.urihttp://hdl.handle.net/10722/164241
 
dc.identifier.volume23
 
dc.languageeng
 
dc.publisherIEEE.
 
dc.publisher.placeUnited States
 
dc.relation.ispartofIEEE Transactions on Neural Networks
 
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 delay
 
dc.typeArticle
 
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<contributor.author>Su, H</contributor.author>
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<subject>Dissipativity</subject>
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