Estimates of equilibrium points and global asymptotic stability of Cohen-Grossberg neural networks with delays

This article studies a class of Cohen-Grossberg neural networks (CGNNs) with variable and distributed delays. Some novel conditions guaranteeing the existence, uniqueness and the estimated location of the equilibrium points are obtained. Using these results, the global asymptotic stability of the CGNNs can be derived without demanding the boundedness and the globally Lipschitz condition of the activation functions. Two numerical examples are demonstrated to verify the theoretical results.