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dc.contributor.authorARSLAN, Emel
dc.date.accessioned2021-12-10T11:30:21Z
dc.date.available2021-12-10T11:30:21Z
dc.identifier.citationARSLAN E., "Novel criteria for global robust stability of dynamical neural networks with multiple time delays", NEURAL NETWORKS, cilt.142, ss.119-127, 2021
dc.identifier.issn0893-6080
dc.identifier.othervv_1032021
dc.identifier.otherav_821bde58-fd25-40b7-92d5-793399bc9bcc
dc.identifier.urihttp://hdl.handle.net/20.500.12627/172019
dc.identifier.urihttps://doi.org/10.1016/j.neunet.2021.04.039
dc.description.abstractThis research article considers the problem regarding global robust asymptotic stability of the general type of dynamical neural networks involving multiple constant time delays. Some new sufficient criteria are proposed for the existence, uniqueness and global asymptotic stability of the equilibrium point of this neural network model whose network parameters possess uncertainties. This paper will first address the existence and uniqueness problem for equilibrium points by utilizing the Homomorphic transformation theory. Secondly, by exploiting a novel Lyapunov functional candidate, the sufficient conditions for asymptotic stability of equilibrium points of this class of delayed neural networks will be established. The derived robust stability conditions are expressed independently of the constant time delay parameters and define some novel relationships among network parameters of the considered neural network. Thus, the applicability and validity of the obtained robust stability conditions for delayed-type neural networks can be easily tested. The comprehensive comparisons among the results of the current article and some of previously derived corresponding results will also be made by giving an illustrative numerical example. (C) 2021 Elsevier Ltd. All rights reserved.
dc.language.isoeng
dc.subjectCellular and Molecular Neuroscience
dc.subjectCognitive Neuroscience
dc.subjectGeneral Neuroscience
dc.subjectNeuroscience (miscellaneous)
dc.subjectSensory Systems
dc.subjectArtificial Intelligence
dc.subjectGeneral Computer Science
dc.subjectHuman-Computer Interaction
dc.subjectComputer Science (miscellaneous)
dc.subjectComputer Vision and Pattern Recognition
dc.subjectPhysical Sciences
dc.subjectLife Sciences
dc.subjectComputer Science Applications
dc.subjectBİLGİSAYAR BİLİMİ, YAPAY ZEKA
dc.subjectBilgisayar Bilimi
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectNEUROSCIENCES
dc.subjectSinirbilim ve Davranış
dc.subjectYaşam Bilimleri (LIFE)
dc.subjectBilgisayar Bilimleri
dc.subjectAlgoritmalar
dc.subjectYaşam Bilimleri
dc.subjectTemel Bilimler
dc.subjectMühendislik ve Teknoloji
dc.subjectDevelopmental Neuroscience
dc.titleNovel criteria for global robust stability of dynamical neural networks with multiple time delays
dc.typeMakale
dc.relation.journalNEURAL NETWORKS
dc.contributor.departmentİstanbul Üniversitesi-Cerrahpaşa , Mühendislik Fakültesi , Bilgisayar Mühendisliği Bölümü
dc.identifier.volume142
dc.identifier.startpage119
dc.identifier.endpage127
dc.contributor.firstauthorID2721901


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