Stability Analysis of Time-Delay Neural Networks Subject to Stochastic Perturbations

This paper is concerned with the problem of mean-square exponential stability of uncertain neural networks with time-varying delay and stochastic perturbation. Both linear and nonlinear stochastic perturbations are considered. The main features of this paper are twofold: 1) Based on generalized Finsler lemma, some improved delay-dependent stability criteria are established, which are more efficient than the existing ones in terms of less conservatism and lower computational complexity; and 2) when the nonlinear stochastic perturbation acting on the system satisfies a class of Lipschitz linear growth conditions, the restrictive condition (or the similar ones) in the existing results can be relaxed under some assumptions. The usefulness of the proposed method is demonstrated by illustrative examples.