The performance of Artificial Bee Colony (ABC) in structure selection of polynomial NARX models

System Identification (SI) is a discipline of building a mathematical model of dynamic systems based on its input and output data. The process of SI is generally divided into structure selection, parameter estimation and model validation. This paper attempts to address the structure selection issue...

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Published in:Journal of Telecommunication, Electronic and Computer Engineering
Main Author: Zabidia A.; Tahira N.M.; Yassin I.M.
Format: Article
Language:English
Published: Universiti Teknikal Malaysia Melaka 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020811964&partnerID=40&md5=44894c8196446d610233d9ba03452f5b
id 2-s2.0-85020811964
spelling 2-s2.0-85020811964
Zabidia A.; Tahira N.M.; Yassin I.M.
The performance of Artificial Bee Colony (ABC) in structure selection of polynomial NARX models
2017
Journal of Telecommunication, Electronic and Computer Engineering
9
1-Apr

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020811964&partnerID=40&md5=44894c8196446d610233d9ba03452f5b
System Identification (SI) is a discipline of building a mathematical model of dynamic systems based on its input and output data. The process of SI is generally divided into structure selection, parameter estimation and model validation. This paper attempts to address the structure selection issue in SI, where the objective is to select the most representative set of regressors to represent the system. However, the selection process must obey the principle of parsimony, where the structure must be as small as possible, yet has the ability to represent the system well. We propose a binarized modification of the Artificial Bee Colony (ABC) algorithm to perform structure selection of a Nonlinear Auto-Regressive with eXogenous (NARX) model on a Direct Current (DC) motor. We compare this implementation with the Binary Particle Swarm Optimization (BPSO) algorithm in terms of solution quality and convergence consistency. The results indicate that the ABC algorithm excelled in terms of convergence consistency with similar solution quality to BPSO algorithm.
Universiti Teknikal Malaysia Melaka
21801843
English
Article

author Zabidia A.; Tahira N.M.; Yassin I.M.
spellingShingle Zabidia A.; Tahira N.M.; Yassin I.M.
The performance of Artificial Bee Colony (ABC) in structure selection of polynomial NARX models
author_facet Zabidia A.; Tahira N.M.; Yassin I.M.
author_sort Zabidia A.; Tahira N.M.; Yassin I.M.
title The performance of Artificial Bee Colony (ABC) in structure selection of polynomial NARX models
title_short The performance of Artificial Bee Colony (ABC) in structure selection of polynomial NARX models
title_full The performance of Artificial Bee Colony (ABC) in structure selection of polynomial NARX models
title_fullStr The performance of Artificial Bee Colony (ABC) in structure selection of polynomial NARX models
title_full_unstemmed The performance of Artificial Bee Colony (ABC) in structure selection of polynomial NARX models
title_sort The performance of Artificial Bee Colony (ABC) in structure selection of polynomial NARX models
publishDate 2017
container_title Journal of Telecommunication, Electronic and Computer Engineering
container_volume 9
container_issue 1-Apr
doi_str_mv
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020811964&partnerID=40&md5=44894c8196446d610233d9ba03452f5b
description System Identification (SI) is a discipline of building a mathematical model of dynamic systems based on its input and output data. The process of SI is generally divided into structure selection, parameter estimation and model validation. This paper attempts to address the structure selection issue in SI, where the objective is to select the most representative set of regressors to represent the system. However, the selection process must obey the principle of parsimony, where the structure must be as small as possible, yet has the ability to represent the system well. We propose a binarized modification of the Artificial Bee Colony (ABC) algorithm to perform structure selection of a Nonlinear Auto-Regressive with eXogenous (NARX) model on a Direct Current (DC) motor. We compare this implementation with the Binary Particle Swarm Optimization (BPSO) algorithm in terms of solution quality and convergence consistency. The results indicate that the ABC algorithm excelled in terms of convergence consistency with similar solution quality to BPSO algorithm.
publisher Universiti Teknikal Malaysia Melaka
issn 21801843
language English
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