Global exponential stability of generalized neural networks with time-varying delays

In this paper, we essentially drop the requirement of Lipschitz condition on the activation functions. Only using physical parameters of neural networks, we propose some new criteria concerning global exponential stability of generalized neutral networks with time-varying delays. Since these new criteria do not require the activation functions to be differentiable, bounded or monotone nondecreasing and the connection weight matrices to be symmetric, they are mild and more general than previously known criteria.