Improved Results on State Estimation for Uncertain Takagi-Sugeno Fuzzy Stochastic Neural Networks with Time-Varying Delays

The delay-dependent state estimation problem for Takagi-Sugeno fuzzy stochastic neural networks with time-varying delays is considered in this paper. We aim to design state estimators to estimate the network states such that the dynamics of the estimation error systems are guaranteed to be exponentially stable in the mean square. Both fuzzy-rule-independent and the fuzzy-rule-dependent state estimators are designed. Delay-dependent sufficient conditions are presented to guarantee the existence of the desired state estimators for the fuzzy stochastic neural networks. Finally, two numerical examples demonstrate that the proposed approaches are effective.