Stability of Markovian Jump Generalized Neural Networks With Interval Time-Varying Delays
DC Field | Value | Language |
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dc.contributor.author | Saravanakumar, Ramasamy | - |
dc.contributor.author | Ali, Muhammed Syed | - |
dc.contributor.author | Ahn, Choon Ki | - |
dc.contributor.author | Karimi, Hamid Reza | - |
dc.contributor.author | Shi, Peng | - |
dc.date.accessioned | 2021-09-03T03:16:38Z | - |
dc.date.available | 2021-09-03T03:16:38Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-08 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/82623 | - |
dc.description.abstract | This paper examines the problem of asymptotic stability for Markovian jump generalized neural networks with interval time-varying delays. Markovian jump parameters are modeled as a continuous-time and finite-state Markov chain. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the linear matrix inequality (LMI) formulation, new delay-dependent stability conditions are established to ascertain the mean-square asymptotic stability result of the equilibrium point. The reciprocally convex combination technique, Jensen's inequality, and the Wirtinger-based double integral inequality are used to handle single and double integral terms in the time derivative of the LKF. The developed results are represented by the LMI. The effectiveness and advantages of the new design method are explained using five numerical examples. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | DISSIPATIVITY ANALYSIS | - |
dc.subject | INFINITY PERFORMANCE | - |
dc.subject | ASYMPTOTIC STABILITY | - |
dc.subject | STATE ESTIMATION | - |
dc.subject | NEUTRAL TYPE | - |
dc.subject | CRITERIA | - |
dc.subject | SYSTEMS | - |
dc.subject | SYNCHRONIZATION | - |
dc.subject | STABILIZATION | - |
dc.subject | DISCRETE | - |
dc.title | Stability of Markovian Jump Generalized Neural Networks With Interval Time-Varying Delays | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ahn, Choon Ki | - |
dc.identifier.doi | 10.1109/TNNLS.2016.2552491 | - |
dc.identifier.scopusid | 2-s2.0-85029697064 | - |
dc.identifier.wosid | 000407058100009 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.28, no.8, pp.1840 - 1850 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.title | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.volume | 28 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 1840 | - |
dc.citation.endPage | 1850 | - |
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 | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | DISSIPATIVITY ANALYSIS | - |
dc.subject.keywordPlus | INFINITY PERFORMANCE | - |
dc.subject.keywordPlus | ASYMPTOTIC STABILITY | - |
dc.subject.keywordPlus | STATE ESTIMATION | - |
dc.subject.keywordPlus | NEUTRAL TYPE | - |
dc.subject.keywordPlus | CRITERIA | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordPlus | SYNCHRONIZATION | - |
dc.subject.keywordPlus | STABILIZATION | - |
dc.subject.keywordPlus | DISCRETE | - |
dc.subject.keywordAuthor | Asymptotic stability | - |
dc.subject.keywordAuthor | generalized neural networks (GNNs) | - |
dc.subject.keywordAuthor | interval time-varying delay | - |
dc.subject.keywordAuthor | Markovian jump parameters | - |
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