Global stability analysis in hopfield neural networks

In this paper, one approach is employed to investigate the existence and uniqueness of the equilibrium and the global attractivity of Hopfield neural network models. Without assuming the boundedness, monotonicity, and differentiability of the activation functions, by using M-matrix theory, Liapunov functions are constructed and employed to establish sufficient conditions for global asymptotic stability.