Intermittent sampled-data control for exponential synchronization of chaotic delayed neural networks via an interval-dependent functional

This paper focuses on the exponential synchronization of chaotic delayed neural networks (CDNNs) via the intermittent sampled-data control (ISC). To save the control cost and limited bandwidth, an ISC strategy which integrates the merits of the intermittent control (IC) scheme and the sampled-data control (SC) scheme is proposed. With the proposed ISC scheme, an interval-dependent Lyapunov functional (IDLF) is established for the synchronization control systems. The main characteristic of this functional is that it takes full advantage of the available state information of both working intervals and sleeping intervals. Due to the continuity of the proposed functional, some restrictive conditions on Lyapunov matrices are removed. In addition, some less conservative synchronization criteria are established by means of a combination of the Lyapunov stability theory, the reciprocally convex inequality and the Wirtinger inequality. Meanwhile, an algorithm for the ISC gain is effectively designed to realize the synchronization of master and slave CDNNs. Furthermore, a qualitative relationship between the duty ratio (DR) and the system decaying rate is analyzed. In the end, two examples are given to reveal that the proposed results are feasible and reasonable.