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 in:	Journal of process control Vol. 73; pp. 123 - 136
Main Authors:	Sammaknejad, Nima, Zhao, Yujia, Huang, Biao
Format:	Journal Article
Language:	English
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
ISSN:	0959-1524, 1873-2771
Online Access:	Get full text
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