Global Asymptotic Stability of Recurrent Neural Networks with Time-Varying Delays and Impulses

Abstract

In this paper, global asymptotic stability of delay recurrent neural networks with time-varying coefficients and impulses are studied by constructing suitable Lyapunov functional. Sufficient conditions, which are independent to the delayed quantity and do not require the response functions to be differentiable, bounded and monotone nondecreasing, are obtained for the global asymptotic stability of the neural networks. Furthermore, an illustrative example and numerical simulation are given to demonstrate the effectiveness with impulse. The numerical simulation shows that under certain condition the dynamic behavior of the system with the influence of the period pulse will produce a Gui-chaotic strange attractor.