Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.

The realistic dynamics mathematical model of a system is very important for analyzing a system. The mathematical system model can be derived by applying physical, thermodynamic, and chemistry laws. But this method has some drawbacks, among which is difficult for complex systems, sometimes is untr...

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Main Author:	Kwad, Ayad Mahmood
Format:	Thesis
Language:	English English English
Published:	2022
Subjects:	QA76.75-76.765 Computer software
Online Access:	http://eprints.uthm.edu.my/8404/1/24p%20AYAD%20MAHMOOD%20KWAD.pdf http://eprints.uthm.edu.my/8404/2/AYAD%20MAHMOOD%20KWAD%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8404/3/AYAD%20MAHMOOD%20KWAD%20WATERMARK.pdf
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spelling	my-uthm-ep.84042023-02-26T02:58:26Z Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models. 2022-03 Kwad, Ayad Mahmood QA76.75-76.765 Computer software The realistic dynamics mathematical model of a system is very important for analyzing a system. The mathematical system model can be derived by applying physical, thermodynamic, and chemistry laws. But this method has some drawbacks, among which is difficult for complex systems, sometimes is untraceable for nonlinear behavior that almost all systems have in the real world, and requires much knowledge. Another method is system identification which is also called experimental modeling. System identification can be made offline, but this method has a disadvantage because the features of a dynamic system may change over time. The parameters may vary as environmental conditions change. It requires big data and consumes a long time. This research introduces a developed method for online system identification based on the Hammerstein and Wiener nonlinear block-oriented structure with the artificial neural networks (NN) advantages and recursive weighted least squares algorithm for optimizing neural network learning in real-time. The proposed method aimed to obtain a maximally informative mathematical model that can describe the actual dynamic behaviors of a system, using the DC motor as a case study. The goodness of fit validation based on the normalized root-mean-square error (NRMSE) and normalized mean square error, and Theil’s inequality coefficient are used to evaluate the performance of models. Based on experimental results, for best Wiener parallel NN model and series-parallel NN model are 93.7% and 89.48%, respectively. Best Hammerstein parallel NN polynomial based model and series-parallel NN polynomial model are 88.75% and 93.9% respectively, for best Hammerstein parallel NN sigmoid based model and series-parallel NN sigmoid based model 78.26% and 95.95% respectively, and for best Hammerstein parallel NN hyperbolic tangent based model and series-parallel NN hyperbolic tangent based model 70.7% and 96.4% respectively. The best model of the developed method outperformed the conventional NARX and NARMAX methods best model by 3.26% in terms of NRMSE goodness of fit. 2022-03 Thesis http://eprints.uthm.edu.my/8404/ http://eprints.uthm.edu.my/8404/1/24p%20AYAD%20MAHMOOD%20KWAD.pdf text en public http://eprints.uthm.edu.my/8404/2/AYAD%20MAHMOOD%20KWAD%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/8404/3/AYAD%20MAHMOOD%20KWAD%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA76.75-76.765 Computer software
spellingShingle	QA76.75-76.765 Computer software Kwad, Ayad Mahmood Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
description	The realistic dynamics mathematical model of a system is very important for analyzing a system. The mathematical system model can be derived by applying physical, thermodynamic, and chemistry laws. But this method has some drawbacks, among which is difficult for complex systems, sometimes is untraceable for nonlinear behavior that almost all systems have in the real world, and requires much knowledge. Another method is system identification which is also called experimental modeling. System identification can be made offline, but this method has a disadvantage because the features of a dynamic system may change over time. The parameters may vary as environmental conditions change. It requires big data and consumes a long time. This research introduces a developed method for online system identification based on the Hammerstein and Wiener nonlinear block-oriented structure with the artificial neural networks (NN) advantages and recursive weighted least squares algorithm for optimizing neural network learning in real-time. The proposed method aimed to obtain a maximally informative mathematical model that can describe the actual dynamic behaviors of a system, using the DC motor as a case study. The goodness of fit validation based on the normalized root-mean-square error (NRMSE) and normalized mean square error, and Theil’s inequality coefficient are used to evaluate the performance of models. Based on experimental results, for best Wiener parallel NN model and series-parallel NN model are 93.7% and 89.48%, respectively. Best Hammerstein parallel NN polynomial based model and series-parallel NN polynomial model are 88.75% and 93.9% respectively, for best Hammerstein parallel NN sigmoid based model and series-parallel NN sigmoid based model 78.26% and 95.95% respectively, and for best Hammerstein parallel NN hyperbolic tangent based model and series-parallel NN hyperbolic tangent based model 70.7% and 96.4% respectively. The best model of the developed method outperformed the conventional NARX and NARMAX methods best model by 3.26% in terms of NRMSE goodness of fit.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Kwad, Ayad Mahmood
author_facet	Kwad, Ayad Mahmood
author_sort	Kwad, Ayad Mahmood
title	Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
title_short	Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
title_full	Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
title_fullStr	Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
title_full_unstemmed	Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
title_sort	online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2022
url	http://eprints.uthm.edu.my/8404/1/24p%20AYAD%20MAHMOOD%20KWAD.pdf http://eprints.uthm.edu.my/8404/2/AYAD%20MAHMOOD%20KWAD%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8404/3/AYAD%20MAHMOOD%20KWAD%20WATERMARK.pdf
_version_	1776103337691709440

Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.

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