Global exponential stability of a class of generalized neural networks with variable coefficients and distributed delays

In this paper, the requirement of Lipschitz condition on the activation functions is essentially dropped. By using Lyapunov functional and Young inequality, some new criteria concerning global exponential stability are obtained for generalized neural networks with variable coefficients and distributed delays. Since these new criteria do not require the activation functions to be differentiable, bounded or monotone non-decreasing and the connection weight matrices to be symmetric, they are mild and more general than previously known criteria.