Global exponential stabilization of neural networks with time delay via impulsive control

The problem of global exponential stabilization of discrete-time delayed neural networks (DDNNs) via impulsive control is addressed in this paper. A novel time-varying Lyapunov functional is proposed to capture the dynamical characteristic of discrete-time impulsive delayed neural networks (DIDNNs). In conjunction with the convex combination technique, new conditions in the form of linear matrix inequalities are established for global exponential stability of DIDNNs. The distinct features of the new stability conditions for DIDNNs are that they are dependent upon the lengths of impulsive intervals but independent of the size of time delay. This paves the way for designing the impulsive controller for impulsive stabilization of DDNNs. The applicability of the developed global exponential stabilization conditions is validated by numerical results.