Global exponential stability of periodic solution of delayed discontinuous Cohen–Grossberg neural networks and its applications
Yiyuan Chai, Jiqiang Feng, Sitian Qin, Xinyu Pan · International Journal of Nonlinear Sciences and Numerical Simulation · 2021
Abstract This paper is concerned with the existence and global exponential stability of the periodic solution of delayed Cohen–Grossberg neural networks (CGNNs) with discontinuous activation functions. The activations considered herein are non-decreasing but not required to be Lipschitz or continuous. Based on differential inclusion theory, Lyapunov functional theory and Leary–Schauder alternative theorem, some sufficient criteria are derived to ensure the existence and global exponential stability of the periodic solution. In order to show the superiority of the obtained results, an application and some detailed comparisons between some existing related results and our results are presented. Finally, some numerical examples are also illustrated.