College of Information Science and Engineering, Northeastern University, Shenyang, China.
IEEE Trans Neural Netw Learn Syst. 2017 Nov;28(11):2626-2637. doi: 10.1109/TNNLS.2016.2599263.
This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.
本文基于增强输入时滞方法研究了具有模态相关区间时变时滞和非周期采样间隔的马尔可夫耦合神经网络的抽样数据同步问题。利用模态相关增广 Lyapunov-Krasovskii 泛函（LKF），使 LKF 矩阵尽可能地模态相关。通过应用扩展 Jensen 积分不等式和 Wirtinger 不等式，得到了新的时滞相关同步准则，充分利用了变采样间隔的上界和变输入时滞的锯齿结构信息。此外，通过求解一组线性矩阵不等式，可以得到期望的随机抽样数据控制器。最后，通过两个实例验证了所提出方法的可行性。
