Mean square exponential stability of stochastic fuzzy Hopfield neural networks with discrete and distributed time-varying delays

It is well known that a complex nonlinear system can be represented as a Takagi–Sugeno (T–S) fuzzy model that consists of a set of linear sub-models. This paper is concerned with the problem of mean square exponential stability for a class of stochastic fuzzy Hopfield neural networks with discrete and distributed time-varying delays. By using the stochastic analysis approach and Ito^ differential formula, delay-dependent conditions ensuring the stability of the considered neural networks are obtained. The conditions are expressed in terms of linear matrix inequalities (LMIs) and can be easily checked by standard software. A numerical example is given to illustrate the effectiveness of the proposed method.