Global exponential stability for recurrent neural networks with a general class of activation functions and variable delays

Based on a general class of activation functions, new results guaranteeing the global exponential stability of the equilibrium for a class of recurrent neural networks with variable delays are obtained. The delayed Hopfield neural network and bidirectional associative memory network and cellular neural networks are special cases of the network model considered in this paper. In addition, we do not require the activation functions to be differentiable, bounded and monotone nondecreasing. So this work gives some improvements to the previous ones.