Simplified Approach To the Exponential Stability of Delayed Neural Networks With Time Varying Delays

dc.contributor.author Singh, Vimal
dc.date.accessioned 2024-07-05T14:33:48Z
dc.date.available 2024-07-05T14:33:48Z
dc.date.issued 2007
dc.description.abstract Sufficient conditions in the form of linear matrix inequalities for the exponential stability of the equilibrium point for delayed neural networks with time varying delays are presented. The conditions turn out to be greatly simplified versions of the exponential stability results previously reported by Yucel and Arik. A distinct feature of the present criteria is that they are free of the degree of exponential stability. This feature makes the criteria computationally very attractive. (c) 2005 Elsevier Ltd. All rights reserved. en_US
dc.identifier.doi 10.1016/j.chaos.2005.11.006
dc.identifier.issn 0960-0779
dc.identifier.issn 1873-2887
dc.identifier.scopus 2-s2.0-33749557213
dc.identifier.uri https://doi.org/10.1016/j.chaos.2005.11.006
dc.identifier.uri https://hdl.handle.net/20.500.14411/956
dc.language.iso en en_US
dc.publisher Pergamon-elsevier Science Ltd en_US
dc.relation.ispartof Chaos, Solitons & Fractals
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject [No Keyword Available] en_US
dc.title Simplified Approach To the Exponential Stability of Delayed Neural Networks With Time Varying Delays en_US
dc.type Article en_US
dspace.entity.type Publication
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gdc.collaboration.industrial false
gdc.description.department Atılım University en_US
gdc.description.departmenttemp Atilim Univ, Dept Elect Elect Engn, TR-06836 Ankara, Turkey en_US
gdc.description.endpage 616 en_US
gdc.description.issue 2 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.startpage 609 en_US
gdc.description.volume 32 en_US
gdc.description.wosquality Q1
gdc.identifier.openalex W2078435211
gdc.identifier.wos WOS:000242759600040
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gdc.oaire.keywords Stability theory of functional-differential equations
gdc.oaire.keywords Neural networks for/in biological studies, artificial life and related topics
gdc.oaire.popularity 4.596428E-10
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gdc.oaire.sciencefields 0103 physical sciences
gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
gdc.oaire.sciencefields 02 engineering and technology
gdc.oaire.sciencefields 01 natural sciences
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gdc.opencitations.count 8
gdc.plumx.crossrefcites 7
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gdc.scopus.citedcount 7
gdc.virtual.author Sıngh, Vımal
gdc.wos.citedcount 8
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