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Global asymptotic stability analysis of bidirectional associative memory neural networks with constant time delays

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dc.contributor.authorArik, S
dc.contributor.authorTavsanoglu, V
dc.date.accessioned2026-06-27T13:04:44Z
dc.date.issued2005
dc.description.abstractThis paper presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with fixed time delays. The results impose constraint conditions on the network parameters of neural system independent of the delay parameters. The results are applicable to all continuous non-monotonic neuron activation functions. The results are also compared with the previously reported results in the literature, implying that the results obtained in this paper provide one more set of criteria for determining the stability of bidirectional associative memory neural networks with time delays. (c) 2005 Elsevier B.V. All rights reserved.en
dc.description.urihttps://doi.org/10.1016/j.neucom.2004.12.002
dc.identifier.doi10.1016/j.neucom.2004.12.002
dc.identifier.eissn1872-8286
dc.identifier.endpage176
dc.identifier.issn0925-2312
dc.identifier.startpage161
dc.identifier.urihttps://hdl.handle.net/20.500.14981/49402
dc.identifier.volume68
dc.identifier.wos000232262900009
dc.language.isoeng
dc.publisherELSEVIER
dc.relation.ispartofNEUROCOMPUTING
dc.subjectglobal asymptotic stability
dc.subjectLyapunov functionals
dc.subjectBi-directional associative memory neural networks
dc.subjecttime delays
dc.subjectEXPONENTIAL STABILITY
dc.subjectSUFFICIENT CONDITION
dc.subjectCONVERGENCE ANALYSIS
dc.subjectQUALITATIVE-ANALYSIS
dc.subjectEQUILIBRIUM-ANALYSIS
dc.subjectCRITERIA
dc.subjectComputer Science
dc.titleGlobal asymptotic stability analysis of bidirectional associative memory neural networks with constant time delays
dc.typeArticle
dspace.entity.typePublication
local.import.sourceWOS

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