Global exponential convergence of delayed inertial Cohen–Grossberg neural networks - Wu, Yanqiu; Dai, Nina; Tu, Zhengwen; Wang, Liangwei; Tang, Qian (Nonlinear Analysis: Modelling and Control, 2023)
In this paper, the exponential convergence of delayed inertial Cohen–Grossberg neural networks (CGNNs) is studied. Two methods are adopted to discuss the inertial CGNNs, one is expressed as two first-order differential equations by selecting a variable substitution, and the other does not change the order of the system based on the nonreduced-order method. By establishing appropriate Lyapunov function and using inequality techniques, sufficient conditions are obtained to ensure that the discussed model converges exponentially to a ball with the prespecified convergence rate. Finally, two simulation examples are proposed to illustrate the validity of the theorem results.
Keywords: inertial Cohen–Grossberg neural networks; time-varying delays; exponential convergence; convergence rate