A new criterion for exponential stability of uncertain stochastic neural networks with mixed delays
This paper deals with the problem of exponential stability for a class of uncertain stochastic neural networks with both discrete and distributed delays (also called mixed delays). The system possesses time-varying and norm-bounded uncertainties. Based on Lyapunov-Krasovskii functional and stochastic analysis approaches, new stability criteria are presented in terms of linear matrix inequalities to guarantee the delayed neural networks to be robustly exponentially stable in the mean square for all admissible parameter uncertainties. Numerical examples are given to illustrate the effectiveness of the developed techniques. (c) 2007 Elsevier Ltd. All rights reserved.
exponential stability stochastic neural networks norm-bounded uncertainties time delays linear matrix inequalities (lmis)