Easy verifiable sufficient conditions for existence and exponential stability of periodic solution for delay cellular neural networks

Abstract

This paper proposes a class of more general model of recurrent neural networks with functional delay, which has been found more suitable to directly apply. Some novel and sufficient conditions of existence, uniqueness and global exponential stability of periodic solution for the recurrent neural network equations are obtained utilizing the theory of coincidence degree, the Lyapunov functional method and M-matrix. These conditions are simple and easily checkable; two examples are given to illustrate the effectiveness of the new results.