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Adaptive Linear regression, Just-In-Time, Locally weighted regression

Sharma, Shekhar

Supervisor and department: Huang, Biao Chemical and Materials Engineering

Examining committee member and department: Liu, Jinfeng Chemical and Materials Engineering Tavakoli, Mahdi Electrical and Computer Engineering Sharp, David Chemical and Materials Engineering Huang, Biao Chemical and Materials Engineering

Department: Department of Chemical and Materials Engineering

Specialization: Process Control

Date accepted: 2015-09-28T08:19:14Z

Graduation date: 2015-11

Degree: Master of Science

Degree level: Master's

Abstract: A number of industrial processes involve variables that cannot be reliably measuredin real time using online sensors. Many such variables are required as inputs incontrol schemes to ensure safe and efficient plant operation. Laboratory analysis,which is a reliable method of measuring these variables, is slow and infrequent. Thus,mathematical models called soft sensors which can estimate these hard to measurevariables from the abundantly available online process measurements have been used in a number of industrial applications. Among the various soft sensor applications of online prediction, process monitoring, fault detection and isolation, the focus of thisthesis is on online prediction and parameter estimation applications. Just-In-Time JIT modeling is a unique framework wherein a local model is created every time a prediction is required. One of the most critical components of JIT models is the similarity criterion which determines the data used in the local models and their associated weights. To handle nonlinear and time varying systems simultaneously under the JIT framework, a new similarity metric which incorporates time, along with the traditional space distance, to evaluate sample weights, is proposed. Further, a query based method to determine the bandwidth of the local models adaptively, as an alternative to the offline global method, is also developed. Next, the distance-angle similarity criterion used in modeling dynamic systems under the JIT technique is studied. An improved weighing scheme is then proposed which enables a more accurate selection of data for local modeling and provides a better interpretation of results. Again, for this proposed weighing scheme also, an alternative to the global bandwidth estimation, called the point-based method, isproposed. In the field of online soft sensor prediction and parameter estimation applications, adaptive linear regression algorithms such as recursive least squares and moving window least squares are widely used because of their simplicity and ease of implementation. However, these methods are not robust to outlying values. We develop a new robust and adaptive algorithm with a cautious parameter update strategy. The proposed algorithm is also quite flexible and a number of variants are easily formulated. Finally, advantages of the methods are clearly illustrated by applications to numerical examples, experimental data and industrial case studies.

Language: English

DOI: doi:10.7939-R3NP1WQ1P

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. 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.





Autor: Sharma, Shekhar

Fuente: https://era.library.ualberta.ca/


Introducción



DATA DRIVEN SOFT SENSOR DESIGN: JUST-IN-TIME AND ADAPTIVE MODELS by Shekhar Sharma A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Process Control Department of Chemical and Materials Engineering University of Alberta c Shekhar Sharma, 2015 Abstract A number of industrial processes involve variables that cannot be reliably measured in real time using online sensors.
Many such variables are required as inputs in control schemes to ensure safe and efficient plant operation.
Laboratory analysis, which is a reliable method of measuring these variables, is slow and infrequent.
Thus, mathematical models called soft sensors which can estimate these hard to measure variables from the abundantly available online process measurements have been used in a number of industrial applications.
Among the various soft sensor applications of online prediction, process monitoring, fault detection and isolation, the focus of this thesis is on online prediction and parameter estimation applications. Just-In-Time (JIT) modeling is a unique framework wherein a local model is created every time a prediction is required.
One of the most critical components of JIT models is the similarity criterion which determines the data used in the local models and their associated weights.
To handle nonlinear and time varying systems simultaneously under the JIT framework, a new similarity metric which incorporates time, along with the traditional space distance, to evaluate sample weights, is proposed.
Further, a query based method to determine the bandwidth of the local models adaptively, as an alternative to the offline global method, is also developed. Next, the distance-angle similarity criterion used in modeling dynamic systems under the JIT technique is studied.
An improved weighing scheme is then proposed which enables a more accurate selection of data for local modeling and provides a better interpretation of results.
Again, for thi...





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