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dc.contributor.authorJ. Thipchaen_US
dc.contributor.authorP. Niamsupen_US
dc.date.accessioned2018-09-04T09:31:32Z-
dc.date.available2018-09-04T09:31:32Z-
dc.date.issued2013-06-28en_US
dc.identifier.issn16870409en_US
dc.identifier.issn10853375en_US
dc.identifier.other2-s2.0-84879318346en_US
dc.identifier.other10.1155/2013/576721en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84879318346&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/52751-
dc.description.abstractThe global exponential stability for bidirectional associative memory neural networks with time-varying delays is studied. In our study, the lower and upper bounds of the activation functions are allowed to be either positive, negative, or zero. By constructing new and improved Lyapunov-Krasovskii functional and introducing free-weighting matrices, a new and improved delay-dependent exponential stability for BAM neural networks with time-varying delays is derived in the form of linear matrix inequality (LMI). Numerical examples are given to demonstrate that the derived condition is less conservative than some existing results given in the literature. © 2013 J. Thipcha and P. Niamsup.en_US
dc.subjectMathematicsen_US
dc.titleGlobal exponential stability criteria for bidirectional associative memory neural networks with time-varying delaysen_US
dc.typeJournalen_US
article.title.sourcetitleAbstract and Applied Analysisen_US
article.volume2013en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsSouth Carolina Commission on Higher Educationen_US
Appears in Collections:CMUL: Journal Articles

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