A Reinforcement Learning-Empowered Feedback Control System for Industrial Internet of Things

被引：0

作者：

Chen, Xing ^{[1
]}

Hu, Junqin ^{[1
]}

Chen, Zheyi ^{[2
]}

Lin, Bing ^{[3
]}

Xiong, Naixue ^{[4
]}

Min, Geyong ^{[2
]}

机构：

[1] College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

[2] Department of Computer Science, College of Engineering, University of Exeter, Exeter, United Kingdom

[3] College of Physics and Energy, Fujian Normal University, Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials, Fuzhou, China

[4] Department of Mathematics and Computer Science, Northeastern State University, Tahlequah,OK, United States

来源：

IEEE Transactions on Industrial Informatics | 2022年 / 18卷 / 04期

关键词：

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Reinforcement learning - Adaptive control systems - Decision making - Quality of service - Balancing - Feedback control

引用

页码：2724 / 2733

共 6 条

[1] Boundaryless Visible Light Communication System Without Attocell for Industrial Internet of Things
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[5] A Deep Reinforcement Learning-Based Cooperative Traffic Signal System Through Dual-Sensing Max Pressure Control
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[6] Retraction Note: Registry service selection based secured internet of things with imperative control for industrial applications (Cluster Computing, (2019), 22, S5, (12507-12519), 10.1007/s10586-017-1678-6)
Kanagaraju, P.
Nallusamy, N.
Cluster Computing, 2023, 26

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