Globally exponential stabilization of neural networks with mixed time delays via impulsive control

The impulsive stabilization problem of neural networks with discrete time-varying delays and unbounded continuously distributed delays is considered. By using impulse-time-dependent Lyapunov function-based techniques to capture the hybrid structure characteristics of the considered impulsive neural networks, two novel global exponential stability criteria are obtained in terms of linear matrix inequalities, which are capable of dealing with the case where both the continuous and discrete dynamics are unstable. When the original continuous-time delayed neural networks are not stable, sufficient conditions are developed for the design of exponentially stable linear impulsive state feedback controllers. Four numerical examples are given to illustrate the less conservatism and effectiveness of the proposed results.