A review of the Expectation Maximization algorithm in data-driven process identification. (January 2019)

• Record Type: Journal Article
• Authors: Sammaknejad, Nima; Zhao, Yujia; Huang, Biao
• Is Part Of: Journal of process control. Volume 73(2019)
• Page Start: 123
• Page End: 136
• Publication Date: 2019-01
• Subjects: Expectation Maximization algorithm -- Data-driven process identification -- Multiple models -- Switching -- State space -- Time delay -- Hidden Markov Models -- Latent variable models -- Outlier treatment -- Missing data; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2018.12.010
• Languages: English
• ISSNs: 0959-1524

Abstract

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.