Network-Based H∞ Filtering for Descriptor Markovian Jump Systems with a Novel Neural Network Event-Triggered Scheme

被引:0
作者
Wang, Yuzhong [1 ]
Zhang, Tie [2 ,3 ]
Chen, Si [4 ]
Ren, Junchao [5 ]
机构
[1] Northeastern Univ, Dept Math, Shenyang 110819, Liaoning, Peoples R China
[2] Northeastern Univ, Dept Math, Shenyang 110004, Peoples R China
[3] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Peoples R China
[4] Shenyang Normal Univ, Daleobiol, Shenyang 110034, Peoples R China
[5] Northeastern Univ, Inst Syst Sci, Shenyang 110819, Liaoning, Peoples R China
基金
中国国家自然科学基金;
关键词
Descriptor Markovian jump systems; Neural network; event-triggered; H-infinity filter; S FUZZY SYSTEM; COMMUNICATION; DISCRETE; DESIGN;
D O I
10.1007/s11063-020-10417-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper studies network-based H-infinity 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-infinity 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-infinity 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.
引用
收藏
页码:757 / 775
页数:19
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