A new robust stability criterion for dynamical neural networks with multiple time delays

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dc.authorid0000-0002-7621-4350
dc.authorid0000-0002-4390-5139
dc.contributor.authorFaydasıçok, Özlemen_US
dc.contributor.authorArik, Sabrien_US
dc.date.accessioned2015-01-15T23:02:18Z
dc.date.available2015-01-15T23:02:18Z
dc.date.issued2013-01-01
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 investigates the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we derive a new criterion for the robust stability of a class of delayed neural networks by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Different from those previously published conditions in the recent literature, the robust stability result presented in this paper not only establishes a time-independent relationship between the network parameters of the neural network, but also takes into account the number the neurons of the designed neural system. Some illustrative numerical examples are also given to make a detailed comparison between our result and the previously published corresponding results. This comparison proves that our result is new and can be considered an alternative condition to those of the previously reported robust stability results.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationFaydasıçok, Ö. & Arik, S. (2013). A new robust stability criterion for dynamical neural networks with multiple time delays. Neurocomputing, 99, 290-297. doi:10.1016/j.neucom.2012.07.004en_US
dc.identifier.doi10.1016/j.neucom.2012.07.004
dc.identifier.endpage297
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.scopus2-s2.0-84867880034
dc.identifier.scopusqualityQ1
dc.identifier.startpage290
dc.identifier.urihttps://hdl.handle.net/11729/506
dc.identifier.urihttp://dx.doi.org/10.1016/j.neucom.2012.07.004
dc.identifier.volume99
dc.identifier.wosWOS:000311129300029
dc.identifier.wosqualityQ2
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.publisherElsevier Science BVen_US
dc.relation.ispartofNeurocomputingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNeural networksen_US
dc.subjectDelayed systemsen_US
dc.subjectLyapunov functionalsen_US
dc.subjectStability analysisen_US
dc.subjectGlobal asymptotic stabilityen_US
dc.subjectVarying delaysen_US
dc.subjectExponential stabilityen_US
dc.subjectNeutral-typeen_US
dc.subjectDependent stabilityen_US
dc.subjectDistributed delaysen_US
dc.subjectDiscreteen_US
dc.titleA new robust stability criterion for dynamical neural networks with multiple time delaysen_US
dc.typeArticleen_US
dspace.entity.typePublication

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