Exponential Stability of Stochastic Fuzzy Hopfield Neural Networks with Time-Varying Delays and Impulses

In this paper, the model of stochastic fuzzy Hopfield neural networks with time-varying delays and impulses (ISFVDHNNs) is established as a modified Takagi-Sugeno (TS) fuzzy model in which the consequent parts are composed of a set of stochastic Hopfield neural networks with time-varying delays and impulses. Then, the global exponential stability in the mean square for ISFVDHNNs is studied by establishing an impulse fuzzy delay differential inequality. The sufficient condition, which is easily checked in practice by simple algebra methods, has a wider adaptive range and it also extends and improves some results in earlier publications.