Global Exponential Stability of Impulsive Neural Networks With Variable Delay: An LMI Approach

This paper focuses on the problem of global exponential stability analysis of impulsive neural networks with variable delay. Three types of impulses are considered: the impulses are input disturbances; the impulses are ldquoneutralrdquo type (that is, they are neither helpful for stability of neural networks nor destabilizing); and the impulses are stabilizing. For each type of impulses, by using Lyapunov function and Razumikhin-type techniques, the sufficient conditions for global exponential stability are developed in terms of linear matrix inequalities with respect to suitable classes of impulse time sequences. The new sufficient stability conditions do not impose any restriction on the size of time-delay. Numerical examples are given which show our results are less conservative than the existing sufficient stability conditions.