New Criteria on Lagrange Stability of Cellular Neural Network with Mixed Delays

In this paper, the problem on globally exponentially stability in the Lagrange sense for cellular neural networks (CNNs) with both mixed delays and general activation functions is dealt with. Here, the mixed delays consist of time-varying delays and finite distribute delays. Based on assuming that the activation functions are neither bounded nor monotonous or differentiable, several algebra criteria for the globally exponentially stability in the Lagrange sense of CNNs are obtained by virtue of Lyapunov functional, Halanay delay differential inequality and linear matrix inequality. Meanwhile, the detailed estimations of the globally exponentially attractive sets are also given out. The results derived here are more general than that of the existing reference. Finally, a numerical examples is given and analyzed to demonstrate the theoretical results.