Exponential Stability for Cellular Neural Networks with Delay

Abstract

This paper investigates the exponential stability of a class of delayed cellular neural networks (DCNN's). By means of appropriately dividing the network state variables into several subgroups, the new sufficient exponential stability condition is derived by constructing Liapunov functional and using the method of the variation of constant. The condition suitable is associated with some initial values and is represented only by some blocks of the interconnection matrix. An example is discussed to illustrate the results.