Convergence of asymptotic systems in Cohen-Grossberg neural network models with unbounded delays

In this paper, we investigate the convergence of asymptotic systems in non-autonomous Cohen–Grossberg neural network models, which include both infinite discrete time-varying and distributed delays. We derive stability results under conditions where the non-delay terms asymptotically dominate the delay terms. Several examples and a numerical simulation are provided to illustrate the significance and novelty of the main result.