Evolutionary computation for model structure selection in system identification

System identification is a field of study involving the derivation of a mathematical model to explain the dynamical behaviour of a system. One of the steps in system identification is model structure selection which involves the selection of variables and terms of a model. Several important criteria...

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Main Author:	Abd. Samad @ Mahmood, Md. Fahmi
Format:	Thesis
Language:	English
Published:	2009
Subjects:	QA Mathematics TJ Mechanical engineering and machinery
Online Access:	http://eprints.utm.my/id/eprint/13602/1/MdFahmiSamadPFKM2009.pdf
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id	my-utm-ep.13602
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spelling	my-utm-ep.136022018-06-25T08:59:51Z Evolutionary computation for model structure selection in system identification 2009 Abd. Samad @ Mahmood, Md. Fahmi QA Mathematics TJ Mechanical engineering and machinery System identification is a field of study involving the derivation of a mathematical model to explain the dynamical behaviour of a system. One of the steps in system identification is model structure selection which involves the selection of variables and terms of a model. Several important criteria for a desirable model structure include its accuracy in future prediction and model parsimony. A parsimonious model structure is desirable in enabling easy control design. This research explores the use of Evolutionary Computation (EC) in model structure selection. The effectiveness of penalty function in the objective function of EC is investigated. The results show that a suitable penalty function parameter can be achieved by its relation to the smallest estimated and tolerable parameter value. Using this function, an algorithm named Modified Genetic Algorithm (MGA) is proposed as it is able to reduce the possibility of premature convergence. MGA is proven to be more efficient than the original genetic algorithm where it is able to find a parsimonious model within a fixed or even shorter evolution period. Another algorithm, named Deterministic Mutation Algorithm (DMA) is proposed to reduce computational burden and reliance on optimum algorithm parameter setting. DMA is a simpler procedure that is able to assist user to obtain a parsimonious model within a shorter time. All of these system identification techniques are carried out by applying the algorithms to a number of simulated and real-life systems, namely gas furnace, Wölfer sunspot and hairdryer, using discrete-time models. Validations of the model structures are made using correlation tests and cross-validation. 2009 Thesis http://eprints.utm.my/id/eprint/13602/ http://eprints.utm.my/id/eprint/13602/1/MdFahmiSamadPFKM2009.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Mechanical Engineering Faculty of Mechanical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics TJ Mechanical engineering and machinery
spellingShingle	QA Mathematics TJ Mechanical engineering and machinery Abd. Samad @ Mahmood, Md. Fahmi Evolutionary computation for model structure selection in system identification
description	System identification is a field of study involving the derivation of a mathematical model to explain the dynamical behaviour of a system. One of the steps in system identification is model structure selection which involves the selection of variables and terms of a model. Several important criteria for a desirable model structure include its accuracy in future prediction and model parsimony. A parsimonious model structure is desirable in enabling easy control design. This research explores the use of Evolutionary Computation (EC) in model structure selection. The effectiveness of penalty function in the objective function of EC is investigated. The results show that a suitable penalty function parameter can be achieved by its relation to the smallest estimated and tolerable parameter value. Using this function, an algorithm named Modified Genetic Algorithm (MGA) is proposed as it is able to reduce the possibility of premature convergence. MGA is proven to be more efficient than the original genetic algorithm where it is able to find a parsimonious model within a fixed or even shorter evolution period. Another algorithm, named Deterministic Mutation Algorithm (DMA) is proposed to reduce computational burden and reliance on optimum algorithm parameter setting. DMA is a simpler procedure that is able to assist user to obtain a parsimonious model within a shorter time. All of these system identification techniques are carried out by applying the algorithms to a number of simulated and real-life systems, namely gas furnace, Wölfer sunspot and hairdryer, using discrete-time models. Validations of the model structures are made using correlation tests and cross-validation.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abd. Samad @ Mahmood, Md. Fahmi
author_facet	Abd. Samad @ Mahmood, Md. Fahmi
author_sort	Abd. Samad @ Mahmood, Md. Fahmi
title	Evolutionary computation for model structure selection in system identification
title_short	Evolutionary computation for model structure selection in system identification
title_full	Evolutionary computation for model structure selection in system identification
title_fullStr	Evolutionary computation for model structure selection in system identification
title_full_unstemmed	Evolutionary computation for model structure selection in system identification
title_sort	evolutionary computation for model structure selection in system identification
granting_institution	Universiti Teknologi Malaysia, Faculty of Mechanical Engineering
granting_department	Faculty of Mechanical Engineering
publishDate	2009
url	http://eprints.utm.my/id/eprint/13602/1/MdFahmiSamadPFKM2009.pdf
_version_	1747814967575314432

Evolutionary computation for model structure selection in system identification

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