Nonstationary and Multirate Process Monitoring by Using Common Trends and Multiple Probability Principal Component Analysis
Author:
Affiliation:
1. School of Automation, Central South University, Changsha 410083, China
2. Pengcheng Laboratory, Shenzhen 518000, China
Funder
Ministry of Science and Technology of the People's Republic of China
Natural Science Foundation of??Hunan Province
National Natural Science Foundation of China
Publisher
American Chemical Society (ACS)
Subject
Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c03178
Reference37 articles.
1. A comprehensive hybrid first principles/machine learning modeling framework for complex industrial processes
2. Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework
3. Neural network aided approximation and parameter inference of non-Markovian models of gene expression
4. Data Mining and Analytics in the Process Industry: The Role of Machine Learning
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