On exponential stability of delayed neural networks with globally Lipschitz continuous activation functions

In this paper, based on globally Lipschitz continuous activation functions, new conditions ensuring existence, uniqueness and global exponential stability of the equilibrium point of delayed neural networks are obtained. The delayed Hopfield network and bidirectional associative memory network are special cases of the network model considered in this paper. So this work gives some improvements to the previous ones.