Study on the Global Asymptotic Stability of Hopfield Neural Networks

In this paper, a mathematical model is built, we study the dynamic behavior of Hopfield neural network. The existence and uniqueness of the equilibrium point and the global asymptotic stability of dynamic neural network models are investigated. We do not assume that the signal propagation functions satisfy the Lipschitz condition and do not require them to be bounded, differentiable or strictly increase. These conditions are presented in terms of system parameters and have important leading significance in designs and applications of the GAS for Hopfield neural networks.