New exponential synchronization for delayed chaotic neural networks with reliable sampled-data control

This paper addresses the problem of exponential synchronization for chaotic neural networks with mixed time-varying delays via reliable sampled-data control. By making full use of the usable information on the actual sampling pattern and the nonlinear part condition, an improved Lyapunov-Krasovskii functional (LKF) is chosen. Based on our novelly constructed LKF and relaxed Wirtinger-based integral inequality, a new synchronization criterion is derived to guarantee the exponential stability of the error system. Moreover, the desired reliable sampled-data controller can be designed on account of the proposed optimization algorithm. Finally, the superiorities of obtained result is demonstrate by one numerical example with simulations.