Publication:
New conditions for global stability of neutral-type delayed Cohen–Grossberg neural networks

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dc.contributor.buuauthorÖzcan, Neyir
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentElektrik Elektronik Mühendisliği Bölümü
dc.contributor.researcheridFSP-6686-2022
dc.contributor.scopusid7003726676
dc.date.accessioned2024-02-29T12:42:13Z
dc.date.available2024-02-29T12:42:13Z
dc.date.issued2018-10
dc.description.abstractThis paper carries out a theoretical investigation of the class of neutral-type delayed Cohen-Grossberg neural networks by using the Lyapunov stability theory. By employing a suitable Lyapunov functional candidate, we derive some new delay independent sufficient conditions for the global asymptotic stability of the equilibrium point for the neutral-type Cohen-Grossberg neural networks with time delays. The obtained stability conditions can be completely characterized by the networks parameters of the neutral systems under consideration. Therefore, it is easy to verify the applicability of our results by simply using some algebraic manipulations of the conditions. Some numerical examples are also given to show the effectiveness of the derived analytical results. A detailed comparison between our proposed results and recently reported corresponding stability results is also made, revealing that the conditions given in this paper establish a new set of stability criteria for Neutral-Type Cohen-Grossberg Neural Networks. (C) 2018 Elsevier Ltd. All rights reserved.
dc.identifier.citationÖzcan, N. vd. (2018). ''New conditions for global stability of neutral-type delayed Cohen–Grossberg neural networks''. Neural Networks, 106, 1-7.
dc.identifier.doihttps://doi.org/10.1016/j.neunet.2018.06.009
dc.identifier.endpage7
dc.identifier.issn0893-6080
dc.identifier.issn1879-2782
dc.identifier.pubmed29990758
dc.identifier.scopus2-s2.0-85049455945
dc.identifier.startpage1
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0893608018301916
dc.identifier.urihttps://hdl.handle.net/11452/40099
dc.identifier.volume106
dc.identifier.wos000445015200001
dc.indexed.wosSCIE
dc.language.isoen
dc.publisherElsevier
dc.relation.journalNeural Networks
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectLyapunov stability theorems
dc.subjectNeutral systems
dc.subjectNeural networks
dc.subjectMatrix theory
dc.subjectTime-varying delays
dc.subjectMarkovian jump parameters
dc.subjectExponential stability
dc.subjectDependent stability
dc.subjectDistributed delays
dc.subjectDiscrete
dc.subjectSignals
dc.subjectSystems
dc.subjectAsymptotic stability
dc.subjectLyapunov functions
dc.subjectNeural networks
dc.subjectSystem stability
dc.subjectAlgebraic manipulations
dc.subjectGlobal asymptotic stability
dc.subjectLyapunov functionals
dc.subjectLyapunov stability theorem
dc.subjectLyapunov stability theory
dc.subjectMatrix theory
dc.subjectNeutral systems
dc.subjectTheoretical investigations
dc.subjectStability criteria
dc.subject.emtreeAnalytic method
dc.subject.emtreeArticle
dc.subject.emtreeArtificial neural network
dc.subject.emtreeBrain function
dc.subject.emtreeComparative study
dc.subject.emtreeEquilibrium constant
dc.subject.emtreeFactor analysis
dc.subject.emtreeMolecular stability
dc.subject.emtreeNerve cell
dc.subject.emtreePriority journal
dc.subject.emtreeQuantitative study
dc.subject.emtreeTheoretical model
dc.subject.emtreeAlgorithm
dc.subject.emtreeComputer simulation
dc.subject.emtreeTime factor
dc.subject.meshAlgorithms
dc.subject.meshComputer simulation
dc.subject.meshNeural networks (computer)
dc.subject.meshTime factors
dc.subject.scopusBAM Neural Network; Time Lag; Bidirectional Associative Memory
dc.subject.wosComputer science, artificial intelligence
dc.subject.wosNeurosciences
dc.titleNew conditions for global stability of neutral-type delayed Cohen–Grossberg neural networks
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Elektrik Elektronik Mühendisliği Bölümü
local.indexed.atScopus
local.indexed.atWOS

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