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

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

doi:10.23919/ChiCC.2018.8483416

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 proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	Proceedings of the 37th Chinese Control Conference, CCC 2018
Editors	Xin Chen, Qianchuan Zhao
Publisher	IEEE Computer Society
Pages	6235-6240
Number of pages	6
ISBN (Electronic)	9789881563941
DOIs	https://doi.org/10.23919/ChiCC.2018.8483416
Publication status	Published - 2018 Oct 5
Event	37th Chinese Control Conference, CCC 2018 - Wuhan, China Duration: 2018 Jul 25 → 2018 Jul 27

Publication series

Name	Chinese Control Conference, CCC
Volume	2018-July
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Other

Other	37th Chinese Control Conference, CCC 2018
Country/Territory	China
City	Wuhan
Period	18/7/25 → 18/7/27

Bibliographical note

Funding Information:
VII. ACKNOWLEDGEMENT This work was supported in part by the National Natural Science Foundation of China under Grant 61503094, in part by the Fundamental Research Funds for the Central Universities.

Publisher Copyright:
© 2018 Technical Committee on Control Theory, Chinese Association of Automation.

Keywords

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

Access to Document

10.23919/ChiCC.2018.8483416

Cite this

Guan, C., Sun, D., Fei, Z., & Ahn, C. K. (2018). Dissipativity-Based Event-triggered Filtering for Discrete-Time Neural Network with Unreliable Communication Links. In X. Chen, & Q. Zhao (Eds.), Proceedings of the 37th Chinese Control Conference, CCC 2018 (pp. 6235-6240). Article 8483416 (Chinese Control Conference, CCC; Vol. 2018-July). IEEE Computer Society. https://doi.org/10.23919/ChiCC.2018.8483416

Dissipativity-Based Event-triggered Filtering for Discrete-Time Neural Network with Unreliable Communication Links. / Guan, Chaoxu; Sun, Dong; Fei, Zhongyang et al.
Proceedings of the 37th Chinese Control Conference, CCC 2018. ed. / Xin Chen; Qianchuan Zhao. IEEE Computer Society, 2018. p. 6235-6240 8483416 (Chinese Control Conference, CCC; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Guan, C, Sun, D, Fei, Z & Ahn, CK 2018, Dissipativity-Based Event-triggered Filtering for Discrete-Time Neural Network with Unreliable Communication Links. in X Chen & Q Zhao (eds), Proceedings of the 37th Chinese Control Conference, CCC 2018., 8483416, Chinese Control Conference, CCC, vol. 2018-July, IEEE Computer Society, pp. 6235-6240, 37th Chinese Control Conference, CCC 2018, Wuhan, China, 18/7/25. https://doi.org/10.23919/ChiCC.2018.8483416

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title = "Dissipativity-Based Event-triggered Filtering for Discrete-Time Neural Network with Unreliable Communication Links",

abstract = "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.",

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N1 - Funding Information: VII. ACKNOWLEDGEMENT This work was supported in part by the National Natural Science Foundation of China under Grant 61503094, in part by the Fundamental Research Funds for the Central Universities. Publisher Copyright: © 2018 Technical Committee on Control Theory, Chinese Association of Automation.

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N2 - 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.

AB - 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.

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

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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