Exponential stability of discrete‐time delayed neural networks with saturated impulsive control

This paper examines the problem of locally exponentially stability for impulsive discrete-time delayed neural networks (IDDNNs) with actuator saturation. By fully considering delay information state considered system, a new delay-dependent polytopic representation within framework is obtained. Based on delay-independent approach, saturation term expressed as convex combination. In order to obtain some less conservative conditions and estimate larger domain attraction, novel type Lyapunov–Krasovskii function (LKF) dependent impulses instant proposed, which called time-dependent LKF. Then, by combining proposed LKF, discrete Wirtinger-based inequality, an extended reciprocally matrix inequality analysis techniques, several exponential criteria bounds are presented. Moreover, when constraints not in controller, system also discussed. Finally, two examples given confirm applicability results.