A review of the Expectation Maximization algorithm in data-driven process identification

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 conditio...

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Bibliographic Details
Published inJournal of process control Vol. 73; pp. 123 - 136
Main Authors Sammaknejad, Nima, Zhao, Yujia, Huang, Biao
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.01.2019
Subjects
Data-driven process identification
Expectation Maximization algorithm
Hidden Markov Models
Latent variable models
Missing data
Multiple models
Outlier treatment
State space
Switching
Time delay
Data-driven process identification
Missing data
Multiple models
Latent variable models
Outlier treatment
State space
Time delay
Hidden Markov Models
Expectation Maximization algorithm
Switching
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Summary: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.
ISSN:0959-1524
1873-2771
DOI:10.1016/j.jprocont.2018.12.010