Comparison between PSO, NE, QR, SVD methods for least squares DC motor identification

Comparison between PSO, NE, QR, SVD methods for least squares DC motor identification

This paper explores the application of the Particle Swarm Optimization (PSO) algorithm for parameter estimation of a Nonlinear Auto-Regressive with Exogeneous Model (NARX) of a Direct Current (DC) motor. The two-step identification step consists of structure selection and parameter estimation. The s...

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Bibliographic Details
Published in:ISCAIE 2015 - 2015 IEEE Symposium on Computer Applications and Industrial Electronics
Main Author: Abdullah S.M.; Yassin I.M.; Tahir N.M.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2015
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959064884&doi=10.1109%2fISCAIE.2015.7298337&partnerID=40&md5=864dc2d19db673d2bc9e0f6e0fbaaf8b
id 2-s2.0-84959064884
spelling 2-s2.0-84959064884
Abdullah S.M.; Yassin I.M.; Tahir N.M.
Comparison between PSO, NE, QR, SVD methods for least squares DC motor identification
2015
ISCAIE 2015 - 2015 IEEE Symposium on Computer Applications and Industrial Electronics


10.1109/ISCAIE.2015.7298337
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959064884&doi=10.1109%2fISCAIE.2015.7298337&partnerID=40&md5=864dc2d19db673d2bc9e0f6e0fbaaf8b
This paper explores the application of the Particle Swarm Optimization (PSO) algorithm for parameter estimation of a Nonlinear Auto-Regressive with Exogeneous Model (NARX) of a Direct Current (DC) motor. The two-step identification step consists of structure selection and parameter estimation. The structure selection process was based on methods from our previous works, while the parameters were estimated using PSO. The propose algorithm was compared with several popular Linear Least Squares (LLS) estimation methods (Normal Equation (NE), QR Factorization (QR) and Singular Value Decomposition (SVD)) found to be comparable with them. © 2015 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Abdullah S.M.; Yassin I.M.; Tahir N.M.
spellingShingle Abdullah S.M.; Yassin I.M.; Tahir N.M.
Comparison between PSO, NE, QR, SVD methods for least squares DC motor identification
author_facet Abdullah S.M.; Yassin I.M.; Tahir N.M.
author_sort Abdullah S.M.; Yassin I.M.; Tahir N.M.
title Comparison between PSO, NE, QR, SVD methods for least squares DC motor identification
title_short Comparison between PSO, NE, QR, SVD methods for least squares DC motor identification
title_full Comparison between PSO, NE, QR, SVD methods for least squares DC motor identification
title_fullStr Comparison between PSO, NE, QR, SVD methods for least squares DC motor identification
title_full_unstemmed Comparison between PSO, NE, QR, SVD methods for least squares DC motor identification
title_sort Comparison between PSO, NE, QR, SVD methods for least squares DC motor identification
publishDate 2015
container_title ISCAIE 2015 - 2015 IEEE Symposium on Computer Applications and Industrial Electronics
container_volume
container_issue
doi_str_mv 10.1109/ISCAIE.2015.7298337
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959064884&doi=10.1109%2fISCAIE.2015.7298337&partnerID=40&md5=864dc2d19db673d2bc9e0f6e0fbaaf8b
description This paper explores the application of the Particle Swarm Optimization (PSO) algorithm for parameter estimation of a Nonlinear Auto-Regressive with Exogeneous Model (NARX) of a Direct Current (DC) motor. The two-step identification step consists of structure selection and parameter estimation. The structure selection process was based on methods from our previous works, while the parameters were estimated using PSO. The propose algorithm was compared with several popular Linear Least Squares (LLS) estimation methods (Normal Equation (NE), QR Factorization (QR) and Singular Value Decomposition (SVD)) found to be comparable with them. © 2015 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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