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.

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。