NARMAX identification of DC motor model using repulsive particle swarm optimization

NARMAX identification of DC motor model using repulsive particle swarm optimization

This paper explores the usage of repulsive particle swarm optimization (RPSO) to perform Non-linear Auto-Regressive with exogenous input (NARMAX) system identification of Direct Current (DC) motor. The NARMAX model was constructed using a recurrent Artificial Neural Network (ANN) model by Rahim and...

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Published in:Proceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009
Main Author: Supeni E.; Yassin I.M.; Ahmad A.; Abdul Rahman F.Y.
Format: Conference paper
Language:English
Published: 2009
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349898532&doi=10.1109%2fCSPA.2009.5069176&partnerID=40&md5=9f380f5aa252ee9f234b2b81df2f16b3
id 2-s2.0-70349898532
spelling 2-s2.0-70349898532
Supeni E.; Yassin I.M.; Ahmad A.; Abdul Rahman F.Y.
NARMAX identification of DC motor model using repulsive particle swarm optimization
2009
Proceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009


10.1109/CSPA.2009.5069176
https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349898532&doi=10.1109%2fCSPA.2009.5069176&partnerID=40&md5=9f380f5aa252ee9f234b2b81df2f16b3
This paper explores the usage of repulsive particle swarm optimization (RPSO) to perform Non-linear Auto-Regressive with exogenous input (NARMAX) system identification of Direct Current (DC) motor. The NARMAX model was constructed using a recurrent Artificial Neural Network (ANN) model by Rahim and Taib and Yassin et al. The comparison result was made between RPSO method and inertia weight-based PSO method by Yassin et al. to train the NARMAX model .The result shows that RPSO yielded comparable performance to the inertia weight-based PSO method in determining NARMAX coefficients in the model. ©2009 IEEE.


English
Conference paper
author Supeni E.; Yassin I.M.; Ahmad A.; Abdul Rahman F.Y.
spellingShingle Supeni E.; Yassin I.M.; Ahmad A.; Abdul Rahman F.Y.
NARMAX identification of DC motor model using repulsive particle swarm optimization
author_facet Supeni E.; Yassin I.M.; Ahmad A.; Abdul Rahman F.Y.
author_sort Supeni E.; Yassin I.M.; Ahmad A.; Abdul Rahman F.Y.
title NARMAX identification of DC motor model using repulsive particle swarm optimization
title_short NARMAX identification of DC motor model using repulsive particle swarm optimization
title_full NARMAX identification of DC motor model using repulsive particle swarm optimization
title_fullStr NARMAX identification of DC motor model using repulsive particle swarm optimization
title_full_unstemmed NARMAX identification of DC motor model using repulsive particle swarm optimization
title_sort NARMAX identification of DC motor model using repulsive particle swarm optimization
publishDate 2009
container_title Proceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009
container_volume
container_issue
doi_str_mv 10.1109/CSPA.2009.5069176
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349898532&doi=10.1109%2fCSPA.2009.5069176&partnerID=40&md5=9f380f5aa252ee9f234b2b81df2f16b3
description This paper explores the usage of repulsive particle swarm optimization (RPSO) to perform Non-linear Auto-Regressive with exogenous input (NARMAX) system identification of Direct Current (DC) motor. The NARMAX model was constructed using a recurrent Artificial Neural Network (ANN) model by Rahim and Taib and Yassin et al. The comparison result was made between RPSO method and inertia weight-based PSO method by Yassin et al. to train the NARMAX model .The result shows that RPSO yielded comparable performance to the inertia weight-based PSO method in determining NARMAX coefficients in the model. ©2009 IEEE.
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language English
format Conference paper
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record_format scopus
collection Scopus
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