This article is concerned with the issue of dynamic event-based non-fragile dissipative state estimation for a type of stochastic complex networks (CNs) subject to a randomly varying coupling as well as fading measurements, where the variation of coupling is governed by a Markov chain. To characterize the measurement fading phenomenon for different nodes, a Rice fading model is considered with known statistics information of the coefficients. For the sake of further resource saving, a dynamic event-triggering strategy (ETS), which is proved to release less data packets than the static one, is implemented to govern the measurements transmission for each sensor to its corresponding estimator. The main objective of this article is to determine a dynamic event-based non-fragile estimator such that, for all possible parameter fluctuations in estimator gains, the estimation error system is stochastically stable with a strict ( $\Upsilon _{1}, \Upsilon _{2}, \Upsilon _{3}$ )- $\gamma $ -dissipativity. Through intensive stochastic analysis, sufficient conditions are then derived in terms LMI to guarantee the existence of the desired state estimator. Finally, the effectiveness of the proposed results are verified by two practical examples of Chua’s circuit and quadruple-tank process system (QTPS).

中文翻译：

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具有衰落测量的量化复杂网络的基于动态事件的非脆弱耗散状态估计及其应用

本文关注的是一类随机复杂网络 (CN) 的基于动态事件的非脆弱耗散状态估计问题，该网络受到随机变化的耦合和衰落测量，其中耦合的变化由马尔可夫控制链。为了表征不同节点的测量衰落现象，考虑了具有已知系数统计信息的莱斯衰落模型。为了进一步节省资源，实施了一种动态事件触发策略（ETS），该策略被证明比静态事件释放更少的数据包，用于管理每个传感器到其相应估计器的测量传输。本文的主要目标是确定一个基于动态事件的非脆弱估计量，使得，对于估计器增益中所有可能的参数波动，估计误差系统是随机稳定的，具有严格的 ( $\Upsilon _{1}, \Upsilon _{2}, \Upsilon _{3}$ )- $\gamma $ -dissipativity . 通过密集的随机分析，然后根据 LMI 导出充分条件以保证所需状态估计量的存在。最后，通过蔡氏电路和四槽处理系统（QTPS）的两个实例验证了所提出结果的有效性。