Particle swarm optimization for NARX structure selection: application on DC motor model / Mohd Ikhwan Abdullah

This thesis was presents the nonlinear identification of a DC motor using Binary Particle Swarm Optimization (BPSO) algorithm, as a model structure selection method, replacing the typical Orthogonal Least Squares (OLS) used in system identification. The BPSO algorithm is an evolutionary computing te...

Full description

Saved in:

Bibliographic Details
Main Author:	Abdullah, Mohd Ikhwan
Format:	Thesis
Language:	English
Published:	2010
Online Access:	https://ir.uitm.edu.my/id/eprint/84616/1/84616.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.84616
record_format	uketd_dc
spelling	my-uitm-ir.846162024-02-07T02:47:00Z Particle swarm optimization for NARX structure selection: application on DC motor model / Mohd Ikhwan Abdullah 2010 Abdullah, Mohd Ikhwan This thesis was presents the nonlinear identification of a DC motor using Binary Particle Swarm Optimization (BPSO) algorithm, as a model structure selection method, replacing the typical Orthogonal Least Squares (OLS) used in system identification. The BPSO algorithm is an evolutionary computing technique put forward by (Kennedy and Eberhart, 1997). By representing its particles technique as probabilities of change (bit flip) of a binary string, the binary string was then used to select a set of repressor as the model structure, and the parameter estimated using QR decomposition. The DC motor dataset was simulated to test the performance of the new model structure selection approach. The findings indicate that the BPSO-based selection method has the potential to become an excellent and effective method to determine parsimonious NARX model structure in the system identification model. The NARX model structure was used to model the system dynamic. The several optimizations on the neural network training have been performed and several regularizations of the training have been considered. Similar investigate in nonlinear identification as done in its linear counterpart. The NARX coupled with suitable regularisations have outperformed the linear models. Even though the linear models are sufficient for this system, the nonlinear model can represent the system better. 2010 Thesis https://ir.uitm.edu.my/id/eprint/84616/ https://ir.uitm.edu.my/id/eprint/84616/1/84616.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Yassin, Ahmad Ihsan
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Yassin, Ahmad Ihsan
description	This thesis was presents the nonlinear identification of a DC motor using Binary Particle Swarm Optimization (BPSO) algorithm, as a model structure selection method, replacing the typical Orthogonal Least Squares (OLS) used in system identification. The BPSO algorithm is an evolutionary computing technique put forward by (Kennedy and Eberhart, 1997). By representing its particles technique as probabilities of change (bit flip) of a binary string, the binary string was then used to select a set of repressor as the model structure, and the parameter estimated using QR decomposition. The DC motor dataset was simulated to test the performance of the new model structure selection approach. The findings indicate that the BPSO-based selection method has the potential to become an excellent and effective method to determine parsimonious NARX model structure in the system identification model. The NARX model structure was used to model the system dynamic. The several optimizations on the neural network training have been performed and several regularizations of the training have been considered. Similar investigate in nonlinear identification as done in its linear counterpart. The NARX coupled with suitable regularisations have outperformed the linear models. Even though the linear models are sufficient for this system, the nonlinear model can represent the system better.
format	Thesis
qualification_level	Bachelor degree
author	Abdullah, Mohd Ikhwan
spellingShingle	Abdullah, Mohd Ikhwan Particle swarm optimization for NARX structure selection: application on DC motor model / Mohd Ikhwan Abdullah
author_facet	Abdullah, Mohd Ikhwan
author_sort	Abdullah, Mohd Ikhwan
title	Particle swarm optimization for NARX structure selection: application on DC motor model / Mohd Ikhwan Abdullah
title_short	Particle swarm optimization for NARX structure selection: application on DC motor model / Mohd Ikhwan Abdullah
title_full	Particle swarm optimization for NARX structure selection: application on DC motor model / Mohd Ikhwan Abdullah
title_fullStr	Particle swarm optimization for NARX structure selection: application on DC motor model / Mohd Ikhwan Abdullah
title_full_unstemmed	Particle swarm optimization for NARX structure selection: application on DC motor model / Mohd Ikhwan Abdullah
title_sort	particle swarm optimization for narx structure selection: application on dc motor model / mohd ikhwan abdullah
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2010
url	https://ir.uitm.edu.my/id/eprint/84616/1/84616.pdf
_version_	1794192031379095552

Particle swarm optimization for NARX structure selection: application on DC motor model / Mohd Ikhwan Abdullah

Similar Items