Dissipativity-Based Event-triggered Filtering for Discrete-Time Neural Network with Unreliable Communication Links

Chaoxu Guan, Dong Sun, Zhongyang Fei, Choon Ki Ahn

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper investigates dissipativity-based filtering for discrete-time neural network with stochastic packet dropout in the frame of limited communication capacity network. In order to save communication resource of the network, an event trigger scheme is introduced to govern the transmission of system output, which can effectively reduce the data package sent by the network and save the bandwidth. Moreover, packet dropout phenomenon, which is supposed to be uncertain so as to be more realistic, is taken into account in the network channel from sensor node to filter node. By applying a novel Lyapunov function, sufficient conditions are presented to guarantee the filtering error of the neural network system to be strictly (\mathcal{Q},\mathcal{S},\mathcal{R})-\gamma. dissipative. Furthermore, a filter and corresponding event trigger mechanism are codesigned based on the dissipativity analysis. Finally, a simulation example is presented to illustrate the validity and merits of the proposed filter design strategy.

Original languageEnglish
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9789881563941
Publication statusPublished - 2018 Oct 5
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: 2018 Jul 252018 Jul 27

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927


Other37th Chinese Control Conference, CCC 2018


  • Discrete-time neural network
  • Dissipative filtering
  • Event trigger scheme
  • Network-based system
  • Uncertain packet dropout

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Applied Mathematics
  • Modelling and Simulation


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