Global Exponential Stability Analysis for Uncertain Neural Networks with Discrete and Distributed Time-Varying Delays

In this paper, the global exponential stability is investigated for a class of neural networks with both discrete and distributed delays and norm-bounded uncertainties. The discrete delay considered in this paper is interval-like time-varying delay. By using Lyapunov stable theory and linear matrix inequality, the derived criteria are not only dependent on distributed delay but also on the lower bound and upper bound of discrete time delay. And we donpsilat need the restriction that the derivative of discrete time-varying delay is less than one. A numerical example is given to illustrate the effectiveness and improvement over some existing results.