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

被引:111
作者
Sammaknejad, Nima [1 ]
Zhao, Yujia [1 ]
Huang, Biao [1 ]
机构
[1] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2G6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Expectation Maximization algorithm; Data-driven process identification; Multiple models; Switching; State space; Time delay; Hidden Markov Models; Latent variable models; Outlier treatment; Missing data; HIDDEN MARKOV-MODELS; NONLINEAR PROCESS IDENTIFICATION; MAXIMUM-LIKELIHOOD-ESTIMATION; PARAMETER-VARYING SYSTEMS; PROCESS TRENDS; RECURSIVE-IDENTIFICATION; PREDICTIVE CONTROL; PARTICLE FILTERS; EM ALGORITHM; LPV APPROACH;
D O I
10.1016/j.jprocont.2018.12.010
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:123 / 136
页数:14
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