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Permanent link (DOI): https://doi.org/10.7939/R3XW4821D

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Estimation of ARX models with time varying time delays Open Access

Descriptions

Other title
Subject/Keyword
Markov chain
t-distribution
time delay
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Zhao,Yujia
Supervisor and department
Huang,Biao (Chemical and Materials Engineering)
Examining committee member and department
Huang,Biao (Chemical and Materials Engineering)
Qiu,Zhijun (Civil and Environmental Engineering)
Choi,Phillip (Chemical and Materials Engineering)
Liu,Jinfeng (Chemical and Materials Engineering)
Department
Department of Chemical and Materials Engineering
Specialization
Process control
Date accepted
2015-11-23T15:27:25Z
Graduation date
2016-06
Degree
Master of Science
Degree level
Master's
Abstract
Processes in industry usually encounter time varying time delays as well as outliers in measurement data. These make identification of the process a challenging problem. Thus, a reliable estimation of the time delay and a correct estimation of the noise to include outliers are essential to efficient process identification. In this thesis, time-varying delay is modeled by a Markov chain in order to reflect the correlation between any consecutive delay values. To deal with this problem, two approaches are considered: off-line parameter estimation (batch estimation) and on-line adaptive parameter estimation (recursive estimation). Two statistical frameworks, i.e., the expectation-maximization (EM) algorithm and a full-Bayesian estimation method named as variational Bayesian (VB), are investigated to model the time delay processes. Normally distributed measurement noise is modeled by the Gaussian distribution in the proposed method, while in the presence of large random noises, the robustness of the proposed algorithms is enhanced by modeling the noise as t-distributions. During the iterative estimation procedure, outlying observations are down-weighted by a latent variable of the t-distribution automatically, and hence, minimizing their adverse influence on identification. The proposed algorithms are verified by simulations and experiments. Finally, models based on the proposed algorithms are identified to effectively predict the production rate for the time-delay extraction process used in the oil sands industry.
Language
English
DOI
doi:10.7939/R3XW4821D
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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