New results on global exponential stability for impulsive cellular neural networks with any bounded time-varying delays

In this paper, the issue of global exponential stability for impulsive cellular neural networks (CNN) with time-varying delays is investigated. Based on Lyapunov functions and the Razumikhin technique, some new stability criteria with an exponential convergence rate are derived. Our results show that impulses play an important role in making cellular neural networks globally exponentially stable even if it may be unstable without impulses. Furthermore, the impulsive moments are independent on the upper bound of the delay function used in our results. Hence, compared with the method of Lyapunov functionals as in many previous studies, our results are less conservative and more effective for stability analysis.