Robust stability criteria for discrete-time switched neural networks with various activation functions

This paper is concerned with the problem of robust stabilization and H∞H∞ control for a class of discrete-time switched neural networks with various activation functions and mixed time delays. By using the average dwell time approach together with the multiple Lyapunov function technique, we propose a state feedback controller to guarantee that the switched neural networks is robustly exponentially stable with the disturbance attenuation level γ > 0 about its equilibrium point for all admissible uncertainties. The obtained results are formulated in terms of linear matrix inequalities (LMIs) which can be easily verified by the MATLAB LMI control toolbox. Finally, numerical examples with simulation result are given to illustrate the effectiveness and applicability of the obtained results.