Absolutely exponential stability of Cohen-Grossberg neural networks with unbounded delays

In this paper, the Cohen-Grossberg neural networks (CGNNs) with variable and unbounded delays are studied. By using the Lipschitzian Hadamard Theorem and a property of homeomorphism mapping, some new sufficient conditions are obtained to ensure the existence, uniqueness and stability of the equilibrium point. The activation functions need only to be partially Lipschitz continuous and monotone nondecreasing, but do not require to be bounded or differentiable.