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Permanent link (DOI): https://doi.org/10.7939/R3R95P

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Statistical Approaches to Robust Identification of Multi-modal Processes Open Access

Descriptions

Other title
Subject/Keyword
Robust
Identification
Statistical
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Lu, Yaojie
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Examining committee member and department
Li, Zukui (Chemical and Materials Engineering)
Huang, Biao (Chemical and Materials Engineering)
Prasad, Vinay (Chemical and Materials Engineering)
Sharp, David (Chemical and Materials Engineering)
Department
Department of Chemical and Materials Engineering
Specialization
Process Control
Date accepted
2014-09-29T10:08:40Z
Graduation date
2014-11
Degree
Master of Science
Degree level
Master's
Abstract
In this thesis, time-varying behaviour, nonlinearity and switching dynamics are generally treated as multi-modal behaviour. Two multi-model modelling techniques, i.e., the linear parameter varying (LPV) technique and the switched modelling technique, are investigated to model the multi-modal processes. The robustness of pro-posed algorithms is enhanced by modelling the noise as t distributions. Two statistical approaches, i.e., EM algorithm and variational Bayesian algorithm, are used for process identification. The proposed algorithms are verified by simulations and experiments. Finally, soft sensors based on proposed algorithms are designed to effectively estimate the steam-quality for the once-through steam generators used in the oil sands industry.
Language
English
DOI
doi:10.7939/R3R95P
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Y. Lu and B. Huang, Robust multiple-model LPV approach to nonlinear process identification using mixture t distributions, Journal of Process Control (in press, 2014).

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