期刊
JOURNAL OF PROCESS CONTROL
卷 73, 期 -, 页码 123-136出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2018.12.010
关键词
Expectation Maximization algorithm; Data-driven process identification; Multiple models; Switching; State space; Time delay; Hidden Markov Models; Latent variable models; Outlier treatment; Missing data
资金
- Natural Sciences and Engineering Research Council of Canada
- Alberta Innovates
The Expectation Maximization (EM) algorithm has been widely used for parameter estimation in data driven process identification. EM is an algorithm for maximum likelihood estimation of parameters and ensures convergence of the likelihood function. In presence of missing variables and in ill conditioned problems, EM algorithm greatly assists the design of more robust identification algorithms. Such situations frequently occur in industrial environments. Missing observations due to sensor malfunctions, multiple process operating conditions and unknown time delay information are some of the examples that can resort to the EM algorithm. In this article, a review on applications of the EM algorithm to address such issues is provided. Future applications of EM algorithm as well as some open problems are also provided. (C) 2018 Elsevier Ltd. All rights reserved.
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