Abstract: This paper deals with the problem of passivity
analysis for stochastic neural networks with leakage, discrete and
distributed delays. By using delay partitioning technique, free
weighting matrix method and stochastic analysis technique, several
sufficient conditions for the passivity of the addressed neural
networks are established in terms of linear matrix inequalities
(LMIs), in which both the time-delay and its time derivative can be
fully considered. A numerical example is given to show the
usefulness and effectiveness of the obtained results.
Abstract: This paper deals with the problem of delay-dependent
stability for neural networks with distributed delays. Some new
sufficient condition are derived by constructing a novel
Lyapunov-Krasovskii functional approach. The criteria are
formulated in terms of a set of linear matrix inequalities, this is
convenient for numerically checking the system stability using the
powerful MATLAB LMI Toolbox. Moreover, in order to show the
stability condition in this paper gives much less conservative results
than those in the literature, numerical examples are considered.
Abstract: This paper deals with the problem of stability of
neural networks with leakage, discrete and distributed delays. A
new Lyapunov functional which contains some new double integral
terms are introduced. By using appropriate model transformation
that shifts the considered systems into the neutral-type time-delay
system, and by making use of some inequality techniques,
delay-dependent criteria are developed to guarantee the stability of
the considered system. Finally, numerical examples are provided to
illustrate the usefulness of the proposed main results.
Abstract: By using the estimation of the Cauchy matrix of linear impulsive differential equations and Banach fixed point theorem as well as Gronwall-Bellman’s inequality, some sufficient conditions are obtained for the existence and exponential stability of almost periodic
solution for an impulsive neural networks with distributed delays. An example is presented to illustrate the feasibility and effectiveness of the results.
Abstract: In this paper, the issue of pth moment stability of a class of stochastic neural networks with mixed delays is investigated. By establishing two integro-differential inequalities, some new sufficient conditions ensuring pth moment exponential stability are obtained. Compared with some previous publications, our results generalize some earlier works reported in the literature, and remove some strict constraints of time delays and kernel functions. Two numerical examples are presented to illustrate the validity of the main results.