IJCAS

This paper studies the exponential synchronization of chaotic delayed neural networks (CDNNs) under aperiodic sampled-data control. First, an aperiodic sampled-data controller with exponentially decaying gain is designed to enlarge the maximum sampling period and the maximum allowable delay while still preserving the stability of the closed-loop system. Then, a novel time-dependent Lyapunov functional that consists of the information of the exponential decay rate η is elaborately designed to analyze the stability of the closed-loop system instead of using the common “change of coordinates” method. With the aid of Lyapunov theory and some inequality techniques, the sufficient conditions are established to guarantee the exponential synchronization of master-slave CDNNs. Based on matrix transformation, the equivalent conditions in LMI form are established to design the feedback gain. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed controller and the obtained synchronization criteria.

Download: http://link.springer.com/article/10.1007/s12555-019-0818-6

Keywords: Aperiodic sampled-data control, chaotic delayed neural networks, exponential synchronization, timedependent Lyapunov functionals, time-varying gain.