New robust exponential stability results for discrete-time switched fuzzy neural networks with time delays

This paper provides a novel result on robust exponential stability for a class of uncertain discrete-time switched fuzzy neural networks (DSFNNs) with time-varying delays and parameter uncertainties. By implementing an average dwell time approach with a new Lyapunov–Krasovskii functional, we obtain some delay-dependent sufficient conditions guaranteeing the robust exponential stability of the considered switched fuzzy neural networks. In other words, a class of switching signals specified by the average dwell time is identified to guarantee the exponential stability of the considered DSFNNs. The obtained conditions are formulated in terms of Linear Matrix Inequalities (LMIs) which can be easily verified via the LMI toolbox. Finally, numerical examples with simulation results are provided to illustrate the applicability and usefulness of the obtained results.