Robust stability of stochastic fuzzy impulsive recurrent neural networks with\\ time-varying delays

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varying delays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural networks with time-varying delays. The results are related to the size of delay and impulses. Finally, numerical examples and simulations are given to demonstrate the correctness of the theoretical results.