Robust exponential stability of uncertain discrete-time stochastic neural networks with impulses

Abstract

This paper focuses on the problem of robust exponential stability of discrete-time impulsive neural networks with parametric uncertainties. By Lyapunov function and stochastic analysis approaches, some robust exponential stability criteria are given. Three types of impulses are considered: stabilizing impulses, destabilizing impulses and neutral impulses. The obtained results have shown that discrete-time neural networks with delays can be robustly exponentially stabilized by an appropriate impulse sequences. Finally, a numerical example is given to show the effectiveness of the results.