Discrete analogue of high-order periodic Cohen–Grossberg neural networks with delay

Abstract

In this paper, new discrete analogue of high-order Cohen–Grossberg neural networks with varying delay is obtained by analysis and approximation techniques. The existence of periodic solution for discrete high-order Cohen–Grossberg neural networks with varying delay is studied by continuation theorem of coincidence degree theory, and sufficient condition is given to guarantee global exponential stability of periodic solution. Finally, an example is given to show the effectiveness of the results in this paper.