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Conference Paper: Delay-dependent exponential estimates of stochastic neural networks with time delay
Title | Delay-dependent exponential estimates of stochastic neural networks with time delay |
---|---|
Authors | |
Keywords | Exponential Estimates Linear Matrix Inequalities (Lmis) Stochastic Neural Networks Time Delay |
Issue Date | 2006 |
Publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ |
Citation | Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2006, v. 4232 LNCS, p. 332-341 How to Cite? |
Abstract | This paper is concerned with the exponential estimating problem for a class of stochastic neural networks with time delay. A sufficient condition, which does not only guarantee the exponential stability but also gives the estimates of decay rate and decay coefficient, is established in terms of a new Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique. The estimating procedure is implemented by solving a set of LMIs, which can be checked easily by effective algorithms. A numerical example is provided to illustrate the effectiveness of the theoretical results. © Springer-Verlag Berlin Heidelberg 2006. |
Persistent Identifier | http://hdl.handle.net/10722/158963 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
References |
DC Field | Value | Language |
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dc.contributor.author | Zhan, S | en_US |
dc.contributor.author | Lam, J | en_US |
dc.date.accessioned | 2012-08-08T09:04:49Z | - |
dc.date.available | 2012-08-08T09:04:49Z | - |
dc.date.issued | 2006 | en_US |
dc.identifier.citation | Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2006, v. 4232 LNCS, p. 332-341 | en_US |
dc.identifier.issn | 0302-9743 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/158963 | - |
dc.description.abstract | This paper is concerned with the exponential estimating problem for a class of stochastic neural networks with time delay. A sufficient condition, which does not only guarantee the exponential stability but also gives the estimates of decay rate and decay coefficient, is established in terms of a new Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique. The estimating procedure is implemented by solving a set of LMIs, which can be checked easily by effective algorithms. A numerical example is provided to illustrate the effectiveness of the theoretical results. © Springer-Verlag Berlin Heidelberg 2006. | en_US |
dc.language | eng | en_US |
dc.publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ | en_US |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
dc.subject | Exponential Estimates | en_US |
dc.subject | Linear Matrix Inequalities (Lmis) | en_US |
dc.subject | Stochastic Neural Networks | en_US |
dc.subject | Time Delay | en_US |
dc.title | Delay-dependent exponential estimates of stochastic neural networks with time delay | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Lam, J:james.lam@hku.hk | en_US |
dc.identifier.authority | Lam, J=rp00133 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.scopus | eid_2-s2.0-33750603763 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-33750603763&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 4232 LNCS | en_US |
dc.identifier.spage | 332 | en_US |
dc.identifier.epage | 341 | en_US |
dc.publisher.place | Germany | en_US |
dc.identifier.scopusauthorid | Zhan, S=15052621300 | en_US |
dc.identifier.scopusauthorid | Lam, J=7201973414 | en_US |
dc.identifier.issnl | 0302-9743 | - |