New results for exponential stability of delayed cellular neural networks

Abstract

This brief presents new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks (DCNNs). It is shown that the use of a more general type of Lyapunov-Krasovskii functional enables us to derive new results for exponential stability of the equilibrium point for DCNNs. The results establish a relation between the delay time and the parameters of the network. The results are also compared with one of the most recent results derived in the literature.