Further analysis of global robust stability of neural networks with multiple time delays

Basit Öğe Kaydı
dc.authorid0000-0002-7621-4350
dc.authorid0000-0002-4390-5139
dc.contributor.authorFaydasıçok, Özlemen_US
dc.contributor.authorArik, Sabrien_US
dc.date.accessioned2015-01-15T23:02:04Z
dc.date.available2015-01-15T23:02:04Z
dc.date.issued2012-04
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Electrical-Electronics Engineeringen_US
dc.description.abstractThis paper deals with the problem of the global robust asymptotic stability of the class of dynamical neural networks with multiple time delays. We propose a new alternative sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point under parameter uncertainties of the neural system. We first prove the existence and uniqueness of the equilibrium point by using the Homomorphic mapping theorem. Then, by employing a new Lyapunov functional, the Lyapunov stability theorem is used to establish the sufficient condition for the asymptotic stability of the equilibrium point. The obtained condition is independent of time delays and relies on the network parameters of the neural system only. Therefore, the equilibrium and stability properties of the delayed neural network can be easily checked. We also make a detailed comparison between our result and the previous corresponding results derived in the previous literature. This comparison proves that our result is new and improves some of the previously reported robust stability results. Some illustrative numerical examples are given to show the applicability and advantages of our result.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationFaydasıçok, Ö. & Arik, S. (2012). Further analysis of global robust stability of neural networks with multiple time delays. Journal of The Franklin Institute-Engineering and Applied Mathematics, 349(3), 813-825. doi:10.1016/j.jfranklin.2011.11.007en_US
dc.identifier.doi10.1016/j.jfranklin.2011.11.007
dc.identifier.endpage825
dc.identifier.issn0016-0032
dc.identifier.issn1879-2693
dc.identifier.issue3
dc.identifier.scopus2-s2.0-84857657387
dc.identifier.scopusqualityQ1
dc.identifier.startpage813
dc.identifier.urihttps://hdl.handle.net/11729/447
dc.identifier.urihttp://dx.doi.org/10.1016/j.jfranklin.2011.11.007
dc.identifier.volume349
dc.identifier.wosWOS:000301829800004
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorArik, Sabrien_US
dc.institutionauthorid0000-0002-4390-5139
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofJournal of The Franklin Institute-Engineering and Applied Mathematicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectVarying delaysen_US
dc.subjectExponential stabilityen_US
dc.subjectDistributed delaysen_US
dc.subjectNeutral-typeen_US
dc.subjectDependent stabilityen_US
dc.subjectDiscreteen_US
dc.subjectCriteriaen_US
dc.titleFurther analysis of global robust stability of neural networks with multiple time delaysen_US
dc.typeArticleen_US
dspace.entity.typePublication

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