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-varyingdelays 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-varyingdelays. 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.