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Novel LMI-Based Condition on Global Asymptotic Stability for a Class of Cohen–Grossberg BAM Networks With Extended Activation Functions

contributor authorZhengqiu Zhang
contributor authorJinde Cao
contributor authorDongming Zhou
date accessioned2020-03-12T18:33:31Z
date available2020-03-12T18:33:31Z
date issued2014
identifier issn2162-237X
identifier other6670797.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/962482?show=full
formatgeneral
languageEnglish
publisherIEEE
titleNovel LMI-Based Condition on Global Asymptotic Stability for a Class of Cohen–Grossberg BAM Networks With Extended Activation Functions
typeJournal Paper
contenttypeMetadata Only
identifier padid7995493
subject keywordsasymptotic stability
subject keywordscontent-addressable storage
subject keywordsdelays
subject keywordslinear matrix inequalities
subject keywordsneural nets
subject keywordstransfer functions
subject keywordsCohen-Grossberg BAM networks
subject keywordsCohen-Grossberg bidirectional associative memory neural networks
subject keywordsLMI method
subject keywordsLMI-based condition
subject keywordsLMI-based sufficient condition
subject keywordsextended activation functions
subject keywordsextended global Lipschitz condition
subject keywordsglobal asymptotic stability
subject keywordshomeomorphism theory
subject keywordslinear matrix inequality method
subject keywordsAsymptotic stability
subject keywordsBiological neural networks
subject keywordsBismuth
subject keywordsDelays
identifier doi10.1109/TNNLS.2013.2289855
journal titleNeural Networks and Learning Systems, IEEE Transactions on
journal volume25
journal issue6
filesize640645
citations0


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