Global exponential convergence of delayed inertial Cohen-Grossberg neural networks

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. two simulation are to illustrate the of the theorem results.