Global asymptotic stability for neural network models with distributed delays
AbstractIn 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.Fundação para a Ciência e a Tecnologia (FCT