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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Veröffentlicht in:	Journal of process control Jg. 73; S. 123 - 136
Hauptverfasser:	Sammaknejad, Nima, Zhao, Yujia, Huang, Biao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.01.2019
Schlagworte:	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-Zugang:	Volltext
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