Exponential H ∞ filtering analysis for discrete-time switched neural networks with random delays using sojourn probabilities

Abstract

This paper is concerned with the exponential $H_{\infty}$ filtering problem for a class of discrete-time switched neural networks with random time-varying delays based on the sojourn-probability-dependent method. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with random time-varying delays which are characterized by introducing a Bernoulli stochastic variable. Based on the derived $H_\infty$ performance analysis results, the $H_\infty$ filter design is formulated in terms of Linear Matrix Inequalities (LMIs). Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design procedure.