Stochastic H-infinity filtering for neural networks with leakage delay and mixed time-varying delays
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ali, M. Syed | - |
dc.contributor.author | Saravanakumar, R. | - |
dc.contributor.author | Ahn, Choon Ki | - |
dc.contributor.author | Karimi, Hamid Reza | - |
dc.date.accessioned | 2021-09-03T07:00:37Z | - |
dc.date.available | 2021-09-03T07:00:37Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-05 | - |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/83699 | - |
dc.description.abstract | This paper deals with the problem of H-infinity filtering for stochastic neural networks (SNNs) with a mixed of time-varying interval delays, time-varying distributed delays, and leakage delays. A novel quintuple integral Lyapunov-Krasovskii functional (LKF) is constructed to improve the performance of the SNN. Sufficient criteria can be obtained by applying the linear matrix inequality (LMI) approach and developing a new mathematical analysis, which ensures the filtering error system is asymptotically stable in the mean square. Finally, simulation results are provided to show the superiority and usefulness of the proposed method. (C) 2017 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.subject | STATE ESTIMATION | - |
dc.subject | EXPONENTIAL STABILITY | - |
dc.subject | SYSTEMS | - |
dc.subject | PERFORMANCE | - |
dc.title | Stochastic H-infinity filtering for neural networks with leakage delay and mixed time-varying delays | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ahn, Choon Ki | - |
dc.identifier.doi | 10.1016/j.ins.2017.01.010 | - |
dc.identifier.scopusid | 2-s2.0-85009518319 | - |
dc.identifier.wosid | 000394068100008 | - |
dc.identifier.bibliographicCitation | INFORMATION SCIENCES, v.388, pp.118 - 134 | - |
dc.relation.isPartOf | INFORMATION SCIENCES | - |
dc.citation.title | INFORMATION SCIENCES | - |
dc.citation.volume | 388 | - |
dc.citation.startPage | 118 | - |
dc.citation.endPage | 134 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | STATE ESTIMATION | - |
dc.subject.keywordPlus | EXPONENTIAL STABILITY | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordAuthor | H-infinity filtering | - |
dc.subject.keywordAuthor | Leakage delay | - |
dc.subject.keywordAuthor | Linear matrix inequality | - |
dc.subject.keywordAuthor | Stochastic neural networks | - |
dc.subject.keywordAuthor | Time-varying delay | - |
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