Data-Driven Soft Sensor Modelling with Time Delay Estimation through Bayesian Optimization
Author:
Affiliation:
1. Methanol Branch, Ningxia Coal Co., Ltd.,Yinchuan,China,750411
2. Tsingyun Intelligence (Beijing) Technologies Co., Ltd.,Beijing,China,100085
3. Tsinghua University,Department of Automation,Beijing,China,100084
Funder
National Key R&D Program of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10606543/10606546/10606685.pdf?arnumber=10606685
Reference21 articles.
1. Present research situation, major bottlenecks, and prospect of refinery industry process control;Huang;Acta Automatica Sinica,2017
2. Data-driven soft sensor development based on deep learning technique
3. Novel Bayesian Framework for Dynamic Soft Sensor Based on Support Vector Machine With Finite Impulse Response
4. Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling
5. Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis
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