Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/13162

TitleGeneral criteria for asymptotic and exponential stabilities of neural network models with unbounded delays
Author(s)Oliveira, José J.
Faria, Teresa
KeywordsCohen-Grossberg neural network
Infinite delay
Distributed delay
Global asymptotic stability
Global exponential stability
Issue dateAug-2011
PublisherElsevier
JournalApplied Mathematics and Computation
Abstract(s)For a family of differential equations with infinite delay, we give sufficient conditions for the global asymptotic, and global exponential stability of an equilibrium point. This family includes most of the delayed models of neural networks of Cohen-Grossberg type, with both bounded and unbounded distributed delay, for which general asymptotic and exponential stability criteria are derived. As illustrations, the results are applied to several concrete models studied in the literature, and a comparison of results is given.
TypeArticle
URIhttp://hdl.handle.net/1822/13162
DOI10.1016/j.amc.2011.04.049
ISSN0096-3003
Publisher versionhttp://www.sciencedirect.com/
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals
DMA - Artigos (Papers)

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