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Finite-time Synchronization of Delayed Semi-Markov Neural Networks with Dynamic Event-triggered Scheme
International Journal of Control, Automation and Systems ( IF 2.5 ) Pub Date : 2021-03-30 , DOI: 10.1007/s12555-020-0348-2
Yujing Jin , Wenhai Qi , Guangdeng Zong

In this paper, the finite-time synchronization (FTS) of semi-Markov neural networks (S-MNNs) with time-varying delay is presented. According to the Lyapunov stability theory, a mode-dependent Lyapunov-Krasovskii functional (LKF) is constructed. Compared with the traditional static event triggered scheme (ETS), a dynamic ETS is adopted to adjust the amount of data transmission and reduce the network burden. By using the general free-weighting matrix method (F-WMM) to estimate a single integral term, a less conservative conclusion is proposed in standard linear matrix inequalities (LMIs). Finally, under the comparison of the static ETS and the dynamic ETS, a simulation example verifies the superiority of this method.



中文翻译:

动态事件触发时滞半马尔可夫神经网络的有限时间同步

本文提出了具有时变时滞的半马尔可夫神经网络(S-MNN)的有限时间同步(FTS)。根据Lyapunov稳定性理论,构造了依赖于模式的Lyapunov-Krasovskii泛函(LKF)。与传统的静态事件触发方案(ETS)相比,采用动态ETS来调整数据传输量并减轻网络负担。通过使用通用自由加权矩阵方法(F-WMM)估计单个积分项,在标准线性矩阵不等式(LMI)中提出了一个不太保守的结论。最后,在静态ETS与动态ETS的比较下,通过仿真实例验证了该方法的优越性。

更新日期:2021-03-30
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