Robust global stability of discrete-time recurrent neural networks

This paper establishes new delay-range-dependent, robust global stability for a class of discrete-time recurrent neural networks with interval time-varying delays and norm-bounded time-varying parameter uncertainties. A new Lyapunov-Krasovskii functional is constructed to exhibit the delay-dependent dynamics and compensate for the enlarged time-span. The developed stability method eliminates the need for over bounding and utilizes a smaller number of linear matrix inequality (LMI) decision variables. New and less conservative solutions to the global stability problem are provided in terms of feasibility testing of new parametrized LMIs. Numerical examples are presented to illustrate the effectiveness of the developed technique.