State prediction of distributed parameter systems based on multi-source spatiotemporal information-Reference-Cited by-同舟云学术

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State prediction of distributed parameter systems based on multi-source spatiotemporal information

Published:2022-11 Issue: Volume:119 Page:55-67
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Mu Guoqing,Chen Junghui^ORCID,Liu Jingxiang,Shao Weiming,Zhao Dongya

Funder

Ministry of Science and Technology, Taiwan

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Reference28 articles.

1. A survey of fault diagnosis and fault-tolerant techniques-part I: fault diagnosis with model-based and signal-based approaches;Gao;IEEE Trans. Ind. Electron.,2015

2. Enhancing produce safety: State estimation-based robust adaptive control of a produce wash system;Azimi;J. Process Control,2020

3. Robust probabilistic predictable feature analysis and its application for dynamic process monitoring;Fan;J. Process Control,2022

4. Multi-step-ahead prediction using dynamic recurrent neural networks;Parlos;Neural Netw.,2000

5. Modeling of distributed parameter systems for applications—A synthesized review from time–space separation;Li;J. Process Control,2010

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing Predictive Monitoring of Ethylene Oxychlorination Reactor States through Spatiotemporal Coupling Analysis;Process Safety and Environmental Protection;2024-09

2. Time-space separation-based data driven method for monitoring distributed parameter process with sparse and noisy sensor data;Control Engineering Practice;2024-01

3. Novel complex-valued long short-term memory network integrating variational mode decomposition for soft sensor;Journal of Process Control;2023-09

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