Sampled-data synchronization control for chaotic neural networks subject to actuator saturation

In this paper, the sampled-data control is applied to synchronize chaotic neural networks subject to actuator saturation. By employing a time-dependent Lyapunov functional that captures the characteristic information of actual sampling pattern, we derive a local stability condition for the synchronization error systems. By this condition, we design a sampled-data controller to regionally synchronize the drive neural networks and response neural networks subject to actuator saturation. Moreover, an optimization method is given to design the desired sampled-data controller such that the set of admissible initial conditions is maximized. A numerical example is given to demonstrate the effectiveness and merits of the proposed design technique.