Zobrazeno 1 - 10
of 3 000
pro vyhledávání: '"data dropout"'
Autor:
Shida Liu1 (AUTHOR), Wei Huang1 (AUTHOR), Ye Ren1 (AUTHOR) yeren@ncut.edu.cn, Li Wang1 (AUTHOR), Honghai Ji1 (AUTHOR)
Publikováno v:
Science Progress. Jan-Mar2024, Vol. 107 Issue 1, p1-37. 37p. 4 Diagrams, 4 Charts, 14 Graphs.
Akademický článek
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Publikováno v:
In Journal of the Franklin Institute March 2023 360(4):2568-2593
Autor:
Ghavifekr, Amir A, Ghiasi, Amir R, Badamchizadeh, Mohammad A, Hashemzadeh, Farzad, Fiorini, Paolo
Publikováno v:
European Journal of Control, 41, pp.63-71 (2018)
This paper addresses the stability conditions of the sampled-data teleoperation systems consisting continuous time master, slave, operator, and environment with discrete time controllers over general communication networks. The output signals of the
Externí odkaz:
http://arxiv.org/abs/2008.06683
Autor:
Minghong She, Wenbin Wu
Publikováno v:
IEEE Access, Vol 11, Pp 118161-118169 (2023)
This paper studies efficient output feedback model predictive control (OFMPC) via adaptive event-triggered control (AETC) for the networked control system (NCS) with data dropout and bounded disturbance. First, we adopt AETC in NCS to save limited ne
Externí odkaz:
https://doaj.org/article/d64b6b0935e5487db7ab338523fdfc58
Akademický článek
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Publikováno v:
IET Cyber-Physical Systems, Vol 7, Iss 3, Pp 113-123 (2022)
Abstract These days, networked control systems (NCSs) in which data is transmitted via communication have been actively studied for many potential applications. In an NCS, data dropout degrades control performance depending on network conditions. For
Externí odkaz:
https://doaj.org/article/7bdeb54d8fc848e1942d4d273e96ca5a
Publikováno v:
IET Cyber-Physical Systems, Vol 7, Iss 2, Pp 69-80 (2022)
Abstract The recent development of the communication technology accelerates studies of real‐time networked control systems using networks. The data dropout is essentially unavoidable, especially in wireless networks and it results from transmission
Externí odkaz:
https://doaj.org/article/420be2c2411241c8814c34edf4c05d13
Autor:
Gao, Xianwen, Weng, Yongpeng
Publikováno v:
In Journal of Process Control September 2020 93:1-13
Deep learning models learn to fit training data while they are highly expected to generalize well to testing data. Most works aim at finding such models by creatively designing architectures and fine-tuning parameters. To adapt to particular tasks, h
Externí odkaz:
http://arxiv.org/abs/1809.00193