System identification of flexible plate structure

This research presented an investigation into the performance of system identification using parametric and nonparametric techniques for the identification of a two-dimensional flexible plate structure. The input and output data of the flexible system were acquired through the experimental work usin...

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Main Author:	Madhloom Al-Khafaji, Ali Abdulhussain
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA76 Computer software
Online Access:	http://eprints.utm.my/id/eprint/12681/6/AliAbdulHussainMFKM2010.pdf
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id	my-utm-ep.12681
record_format	uketd_dc
spelling	my-utm-ep.126812017-09-13T01:21:04Z System identification of flexible plate structure 2010 Madhloom Al-Khafaji, Ali Abdulhussain QA76 Computer software This research presented an investigation into the performance of system identification using parametric and nonparametric techniques for the identification of a two-dimensional flexible plate structure. The input and output data of the flexible system were acquired through the experimental work using National Instrumentation data acquisition system and flexible plate test rig. A sinusoidal force was applied to excite the flexible plate and the dynamic response of the system was investigated. The parametric models of the system were developed through Recursive Least Square (RLS) and Genetic Algorithms (GA) methods; whilst the nonparametric models of the system were developed using Multi-layer Perceptron Neural Networks (MLP-NN), Adaptive Elman Neural Networks (ENN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The validity of the models was investigated based on statistical measures, mean square error and one step ahead output prediction. A comparative performance of all the approaches developed in this research was presented and discussed. It has been demonstrated that the best mean squared error for RLS was 0.0095 and for GA algorithm was 0.000562. This indicates the superiority of GA as compared to RLS for the parametric modelling approaches. For the nonparametric modelling of the system, the best mean squared error for MLP-NN, ENN and ANFIS were 0.000163, 0.001700 and 0.0003978, respectively. The results demonstrated that MLP-NN shows superiority as compared to ENN and ANFIS. The investigation also revealed that, comparing to all modelling techniques, MLP-NN performed the best in terms of convergence time to an optimum solution. 2010 Thesis http://eprints.utm.my/id/eprint/12681/ http://eprints.utm.my/id/eprint/12681/6/AliAbdulHussainMFKM2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Mechanical Engineering Faculty of Mechanical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Madhloom Al-Khafaji, Ali Abdulhussain System identification of flexible plate structure
description	This research presented an investigation into the performance of system identification using parametric and nonparametric techniques for the identification of a two-dimensional flexible plate structure. The input and output data of the flexible system were acquired through the experimental work using National Instrumentation data acquisition system and flexible plate test rig. A sinusoidal force was applied to excite the flexible plate and the dynamic response of the system was investigated. The parametric models of the system were developed through Recursive Least Square (RLS) and Genetic Algorithms (GA) methods; whilst the nonparametric models of the system were developed using Multi-layer Perceptron Neural Networks (MLP-NN), Adaptive Elman Neural Networks (ENN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The validity of the models was investigated based on statistical measures, mean square error and one step ahead output prediction. A comparative performance of all the approaches developed in this research was presented and discussed. It has been demonstrated that the best mean squared error for RLS was 0.0095 and for GA algorithm was 0.000562. This indicates the superiority of GA as compared to RLS for the parametric modelling approaches. For the nonparametric modelling of the system, the best mean squared error for MLP-NN, ENN and ANFIS were 0.000163, 0.001700 and 0.0003978, respectively. The results demonstrated that MLP-NN shows superiority as compared to ENN and ANFIS. The investigation also revealed that, comparing to all modelling techniques, MLP-NN performed the best in terms of convergence time to an optimum solution.
format	Thesis
qualification_level	Master's degree
author	Madhloom Al-Khafaji, Ali Abdulhussain
author_facet	Madhloom Al-Khafaji, Ali Abdulhussain
author_sort	Madhloom Al-Khafaji, Ali Abdulhussain
title	System identification of flexible plate structure
title_short	System identification of flexible plate structure
title_full	System identification of flexible plate structure
title_fullStr	System identification of flexible plate structure
title_full_unstemmed	System identification of flexible plate structure
title_sort	system identification of flexible plate structure
granting_institution	Universiti Teknologi Malaysia, Faculty of Mechanical Engineering
granting_department	Faculty of Mechanical Engineering
publishDate	2010
url	http://eprints.utm.my/id/eprint/12681/6/AliAbdulHussainMFKM2010.pdf
_version_	1747814945523761152

System identification of flexible plate structure

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