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Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Rajchakit, G. | - |
| dc.contributor.author | Saravanakumar, R. | - |
| dc.contributor.author | Ahn, Choon Ki | - |
| dc.contributor.author | Karimi, Hamid Reza | - |
| dc.date.accessioned | 2021-09-03T10:35:02Z | - |
| dc.date.available | 2021-09-03T10:35:02Z | - |
| dc.date.created | 2021-06-16 | - |
| dc.date.issued | 2017-02 | - |
| dc.identifier.issn | 0893-6080 | - |
| dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/84827 | - |
| dc.description.abstract | This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov-Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs. The superiority of the obtained results is clearly demonstrated by numerical examples. (C) 2016 Elsevier Ltd. All rights reserved. | - |
| dc.language | English | - |
| dc.language.iso | en | - |
| dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
| dc.subject | DEPENDENT STABILITY-CRITERIA | - |
| dc.subject | DISTURBANCE ATTENUATION | - |
| dc.subject | DISCRETE | - |
| dc.title | Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals | - |
| dc.type | Article | - |
| dc.contributor.affiliatedAuthor | Ahn, Choon Ki | - |
| dc.identifier.doi | 10.1016/j.neunet.2016.10.009 | - |
| dc.identifier.scopusid | 2-s2.0-85001948852 | - |
| dc.identifier.wosid | 000393723000002 | - |
| dc.identifier.bibliographicCitation | NEURAL NETWORKS, v.86, pp.10 - 17 | - |
| dc.relation.isPartOf | NEURAL NETWORKS | - |
| dc.citation.title | NEURAL NETWORKS | - |
| dc.citation.volume | 86 | - |
| dc.citation.startPage | 10 | - |
| dc.citation.endPage | 17 | - |
| 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.journalResearchArea | Neurosciences & Neurology | - |
| dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
| dc.relation.journalWebOfScienceCategory | Neurosciences | - |
| dc.subject.keywordPlus | DEPENDENT STABILITY-CRITERIA | - |
| dc.subject.keywordPlus | DISTURBANCE ATTENUATION | - |
| dc.subject.keywordPlus | DISCRETE | - |
| dc.subject.keywordAuthor | Generalized neural network | - |
| dc.subject.keywordAuthor | Stability analysis | - |
| dc.subject.keywordAuthor | Time-varying delay | - |
| dc.subject.keywordAuthor | Weighted integral inequality | - |
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