Robust Stability Criteria for Uncertain Stochastic Cellular Neural Networks with Time Delays

In this paper, the global robust asymptotic stability problem is considered for stochastic cellular neural networks with time delays and parameter uncertainties. The aim of this paper is to establish easily verifiable conditions under which the stochastic cellular neural networks is globally robustly asymptotically stable in the mean square for all admissible parameter uncertainties. Base on Lyapunov- Krasovskii functional and stochastic analysis approaches, a linear matrix inequality (LMI) approach is developed to derive the stability criteria. A numerical example is provided to illustrate the effectiveness and applicability of the proposed criteria.