Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/35513

TitleGlobal asymptotic stability of nonautonomous Cohen-Grossberg neural network models with infinite delays
Author(s)Salete, Esteves
Oliveira, José J.
KeywordsCohen-Grossberg neural networks
Unbounded time-varying coefficients
Unbounded distributed delays
Global asymptotic stability
Issue date2015
PublisherElsevier
JournalApplied Mathematics and Computation
CitationEsteves, S., & Oliveira, J. J. (2015). Global asymptotic stability of nonautonomous Cohen-Grossberg neural network models with infinite delays. Applied Mathematics and Computation, 265, 333-346. doi: 10.1016/j.amc.2015.04.103
Abstract(s)For a general Cohen-Grossberg neural network model with potentially unbounded time-varying coeffi cients and infi nite distributed delays, we give su fficient conditions for its global asymptotic stability. The model studied is general enough to include, as subclass, the most of famous neural network models such as Cohen-Grossberg, Hopfi eld, and bidirectional associative memory. Contrary to usual in the literature, in the proofs we do not use Lyapunov functionals. As illustrated, the results are applied to several concrete models studied in the literature and a comparison of results shows that our results give new global stability criteria for several neural network models and improve some earlier publications.
TypeArticle
URIhttp://hdl.handle.net/1822/35513
DOI10.1016/j.amc.2015.04.103
ISSN0096-3003
Publisher versionhttp://www.sciencedirect.com/science/journal/00963003/265
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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