Finite time stability for neural networks with supremum.

One of the main properties of solutions of nonlinear neural networks is stability and often the direct Lyapunov method is used to study stability properties. Often the present state depends on its maximum value over a past time interval. It requires the study of a new model, so called model with supremum. Some sufficient conditions for stability of equilibrium of nonlinear neural networks with supremum, time varying self-regulating parameters of all units and time varying functions of the connection between two neurons in the network are obtained. The cases of time varying Lipschitz coefficients as well as non-Lipschitz activation functions are studied. [ABSTRACT FROM AUTHOR]