Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/23929

TitleGlobal exponential stability of nonautonomous neural network models with continuous distributed delays
Author(s)Esteves, Salete
Gokmen, Elçin
Oliveira, José J.
KeywordsHopfield neural network
BAM neural network
Time-varying coefficient
Distributed time delay
Periodic solution
Global exponential stability
Issue date1-May-2013
PublisherElsevier
JournalApplied Mathematics and Computation
Abstract(s)For a family of non-autonomous differential equations with distributed delays, we give sufficient conditions for the global exponential stability of an equilibrium point. This family includes most of the delayed models of neural networks of Hopfield type, with time-varying coefficients and distributed delays. For these models, we establish sufficient conditions for their global exponential stability. The existence and global exponential stability of a periodic solution is also addressed. A comparison of results shows that these results are general, news, and add something new to some earlier publications.
TypeArticle
URIhttp://hdl.handle.net/1822/23929
DOI10.1016/j.amc.2013.03.035
ISSN0096-3003
Publisher versionhttp://www.sciencedirect.com/science/article/pii/S0096300313002889
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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