On exponential stability results for fuzzy impulsive neural networks

Complex nonlinear systems can be represented to a set of linear sub-models by using fuzzy sets and fuzzy reasoning via ordinary Takagi–Sugeno (TS) fuzzy models. In this paper, the exponential stability of TS fuzzy neural networks with impulsive effect and time-varying delays is investigated. The model for fuzzy impulsive neural networks with time-varying delays is first established as a modified TS fuzzy model in which the consequent parts are composed of a set of impulsive neural networks with time-varying delays. Secondly, the exponential stability for fuzzy impulsive neural networks is presented by utilizing the Lyapunov–Krasovskii functional and the linear matrix inequality (LMI) approach. In addition, two numerical examples are provided to illustrate the applicability of the result using LMI control toolbox in MATLAB.