期刊
APPLIED MATHEMATICS AND COMPUTATION
卷 316, 期 -, 页码 205-214出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2017.08.034
关键词
Neural network; State estimation; H-infinity control; Sampling; Transmission delay; Packet dropout
资金
- Basic Science Research Programs through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2017R1A2B2004671]
- Natural Science Foundation of CQ [CSTC 2014J-CYJA40004]
- Natural Science Foundation of China [11471061]
This study considers the network-based H-infinity state estimation problem for neural networks where transmitted measurements suffer from the sampling effect, external disturbance, network-induced delay, and packet dropout as network constraints. The external disturbance, network-induced delay, and packet dropout affect the measurements at only the sampling instants owing to the sampling effect. In addition, when packet dropout occurs, the last received data are used. To tackle the imperfect signals, a compensator is designed, and then by aid of the compensator, H-infinity filter which guarantees desired performance is designed as well. A numerical example is given to illustrate the validity of the proposed methods. (C) 2017 Elsevier Inc. All rights reserved.
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