Exponential Stabilization of Delayed Chaotic Memristive Neural Networks Via Aperiodically Intermittent Control

This paper investigates the exponential stabilization of delayed chaotic memristive neural networks (MNNs) via aperiodically intermittent control. The issue is proposed for two reasons: (1) The control signal may not always exist in practical applications; (2) How to enlarge the maximum allowable failure interval (MAFI) for sensors is a challenging problem. To surmount these difficulties, an index called the largest proportion of the rest width (LPRW) in the control period is proposed to measure the MAFI in the sense of guaranteeing the closed-loop system performance with the least control cost. Then, by constructing suitable Lyapunov functional in combination with interval matrix method and Halanay inequality, a stabilization criterion is established to determine the relationship between the feedback gain and the LPRW. Meanwhile, an algorithm is proposed to qualitatively analyze the relationship between the feedback gain and the LPRW. In contrast with the previous works, our results can increase the value of LPRW while still maintaining the stability of the closed-loop MNNs. Finally, some comparisons of simulation results demonstrate that the obtained stabilization criterion has some advantages over the existing ones.