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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| Published in: | Journal of process control Vol. 73; pp. 123 - 136 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.01.2019
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| Subjects: | |
| ISSN: | 0959-1524, 1873-2771 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Zhao, Yujia Huang, Biao Sammaknejad, Nima |
| Author_xml | – sequence: 1 givenname: Nima surname: Sammaknejad fullname: Sammaknejad, Nima email: sammakne@ualberta.ca – sequence: 2 givenname: Yujia surname: Zhao fullname: Zhao, Yujia email: yujia3@ualberta.ca – sequence: 3 givenname: Biao surname: Huang fullname: Huang, Biao email: biao.huang@ualberta.ca |
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| Snippet | The Expectation Maximization (EM) algorithm has been widely used for parameter estimation in data-driven process identification. EM is an algorithm for maximum... |
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| SubjectTerms | Data-driven process identification Expectation Maximization algorithm Hidden Markov Models Latent variable models Missing data Multiple models Outlier treatment State space Switching Time delay |
| Title | A review of the Expectation Maximization algorithm in data-driven process identification |
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