This paper studies network-based \(H_\infty \) filtering problem for descriptor Markovian jump systems with a novel neural network event-triggered scheme. Firstly, to save more limited communication bandwidth, a novel neural network event-triggered scheme is introduced to dynamically adjust communication bandwidth based on desired filtering performance. Secondly, an event-triggered mode-dependent \(H_\infty \) filter is designed for descriptor Markovian jump system. By considering the network-induced delay and the event-triggered scheme, a delay system method is used to build a novel filtering error system model. By using Lyapunov function technology and free weighting method, the criteria are obtained in terms of LMIs which guarantee the filtering error system to be regular, impulse free and stochastically stable with the \(H_\infty \) performance. Then, a co-design method is proposed for the designed filter parameters. Finally, a numerical simulation example is employed to illustrate the effectiveness, and by a compared example, we show that the number of transmitted data produced by the proposed neural network event-triggered scheme is less than those produced by traditional event-triggered scheme.

本文采用一种新型的神经网络事件触发方案研究了描述性马尔可夫跳跃系统基于网络的（H_ \ infty \）滤波问题。首先，为了节省更多有限的通信带宽，引入了一种新颖的神经网络事件触发方案，以根据所需的过滤性能动态调整通信带宽。其次，事件触发的模式相关\（H_ \ infty \）滤波器是为描述符马尔可夫跳跃系统设计的。通过考虑网络引起的时延和事件触发方案，采用时延系统方法建立了新颖的滤波误差系统模型。通过使用Lyapunov函数技术和自由加权方法，根据LMI获得了标准，这些标准保证了滤波误差系统是规则的，无脉冲的并且具有\（H_ \ infty \）性能随机稳定。然后，针对设计的滤波器参数提出了一种协同设计方法。最后，通过一个数值仿真例子来说明其有效性，并通过一个比较例表明，所提出的神经网络事件触发方案所产生的传输数据数量要少于传统事件触发方案所产生的数据传输数量。