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Novel results for global robust stability of delayed neural networks

dc.contributor.authorYucel, Eylem
dc.contributor.authorArik, Sabri
dc.date.accessioned2021-03-06T11:05:37Z
dc.date.available2021-03-06T11:05:37Z
dc.date.issued2009
dc.identifier.citationYucel E., Arik S., "Novel results for global robust stability of delayed neural networks", CHAOS SOLITONS & FRACTALS, cilt.39, ss.1604-1614, 2009
dc.identifier.issn0960-0779
dc.identifier.otherav_ed758277-c829-46ca-b60d-41e8e91e66e3
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/155894
dc.identifier.urihttps://doi.org/10.1016/j.chaos.2007.06.052
dc.description.abstractThis paper investigates the global robust convergence properties of continuous-time neural networks with discrete time delays. By employing suitable Lyapunov functionals, some sufficient conditions for the existence, uniqueness and global robust asymptotic stability of the equilibrium point are derived. The conditions can be easily verified as they can be expressed in terms of the network parameters only. Some numerical examples are also given to compare our results with previous robust stability results derived in the literature. (C) 2007 Elsevier Ltd. All rights reserved.
dc.language.isoeng
dc.subjectFizik
dc.subjectFİZİK, MATEMATİK
dc.subjectDisiplinlerarası Fizik ve İlgili Bilim ve Teknoloji Alanları
dc.subjectMATEMATİK, İNTERDİSKÜP UYGULAMALAR
dc.subjectMatematik
dc.subjectTemel Bilimler (SCI)
dc.subjectFİZİK, MULTİDİSİPLİNER
dc.subjectGenel Fizik
dc.subjectTemel Bilimler
dc.titleNovel results for global robust stability of delayed neural networks
dc.typeMakale
dc.relation.journalCHAOS SOLITONS & FRACTALS
dc.contributor.departmentİstanbul Üniversitesi , Mühendislik Fakültesi , Bilgisayar Mühendisliği
dc.identifier.volume39
dc.identifier.issue4
dc.identifier.startpage1604
dc.identifier.endpage1614
dc.contributor.firstauthorID57561


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