Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/13164

TitleGlobal asymptotic stability for neural network models with distributed delays
Author(s)Oliveira, José J.
KeywordsDelayed neural network models
Distributed delays
Time-varing delays
Global asymptotic stability
M-matrix
Time-varying delays
Issue dateJul-2009
PublisherElsevier
JournalMathematical and Computer Modelling
Abstract(s)In this paper, we obtain the global asymptotic stability of the zero solution of a general n-dimensional delayed differential system, by imposing a condition of dominance of the nondelayed terms which cancels the delayed effect. We consider several delayed differential systems in general settings, which allow us to study, as subclasses, the well known neural network models of Hopfield, Cohn-Grossberg, bidirectional associative memory, and static with S-type distributed delays. For these systems, we establish sufficient conditions for the existence of a unique equilibrium and its global asymptotic stability, without using the Lyapunov functional technique. Our results improve and generalize some existing ones.
TypeArticle
URIhttp://hdl.handle.net/1822/13164
DOI10.1016/j.mcm.2009.02.002
ISSN0895-7177
Publisher versionhttp://www.sciencedirect.com
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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