TY - GEN
T1 - Dissipativity-Based Event-triggered Filtering for Discrete-Time Neural Network with Unreliable Communication Links
AU - Guan, Chaoxu
AU - Sun, Dong
AU - Fei, Zhongyang
AU - Ahn, Choon Ki
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.
PY - 2018/10/5
Y1 - 2018/10/5
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.
KW - Discrete-time neural network
KW - Dissipative filtering
KW - Event trigger scheme
KW - Network-based system
KW - Uncertain packet dropout
UR - http://www.scopus.com/inward/record.url?scp=85056078992&partnerID=8YFLogxK
U2 - 10.23919/ChiCC.2018.8483416
DO - 10.23919/ChiCC.2018.8483416
M3 - Conference contribution
AN - SCOPUS:85056078992
T3 - Chinese Control Conference, CCC
SP - 6235
EP - 6240
BT - Proceedings of the 37th Chinese Control Conference, CCC 2018
A2 - Chen, Xin
A2 - Zhao, Qianchuan
PB - IEEE Computer Society
T2 - 37th Chinese Control Conference, CCC 2018
Y2 - 25 July 2018 through 27 July 2018
ER -