Global equilibrium stability of discrete-time analog neural networks

In this paper, some global stability criteria of an equilibrium state for a general class of discrete-time dynamic neural networks are presented using a novel diagonal Lyapunov function approach, and the resulting criteria are described by the diagonal Lyapunov matrix equations. First, globally diagonal Lyapunov function approaches are applied to study equilibrium stability problem of a class of discrete-time dynamic neural networks without linear terms. Some novel stability conditions are then obtained for a general class of discrete-time dynamic neural networks