Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure

This paper focused on modelling of a gradient flexible plate system utilizing an evolutionary algorithm, namely particle swarm optimization (PSO) and cuckoo search (CS) algorithm. A square aluminium plate experimental rig with a gradient of 30° and all edges clamped were designed and fabricated to a...

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Published in:International Journal of Automotive and Mechanical Engineering
Main Author: Jamali A.; Hassan M.H.; Lidyana R.; Hadi M.S.
Format: Article
Language:English
Published: Universiti Malaysia Pahang 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174965341&doi=10.15282%2fijame.20.3.2023.01.0815&partnerID=40&md5=02af921d273a60bfc3257e08a104d491
id 2-s2.0-85174965341
spelling 2-s2.0-85174965341
Jamali A.; Hassan M.H.; Lidyana R.; Hadi M.S.
Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure
2023
International Journal of Automotive and Mechanical Engineering
20
3
10.15282/ijame.20.3.2023.01.0815
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174965341&doi=10.15282%2fijame.20.3.2023.01.0815&partnerID=40&md5=02af921d273a60bfc3257e08a104d491
This paper focused on modelling of a gradient flexible plate system utilizing an evolutionary algorithm, namely particle swarm optimization (PSO) and cuckoo search (CS) algorithm. A square aluminium plate experimental rig with a gradient of 30° and all edges clamped were designed and fabricated to acquire input-output vibration data experimentally. This input-output data was then applied in a system identification method, which used an evolutionary algorithm with a linear autoregressive with exogenous (ARX) model structure to generate a dynamic model of the system. The obtained results were then compared with the conventional method that is recursive least square (RLS). The developed models were evaluated based on the lowest mean square error (MSE), within the 95% confidence level of both auto and cross-correlation tests as well as high stability in the pole-zero diagram. Investigation of results indicates that both evolutionary algorithms provide lower MSE than RLS. It is demonstrated that intelligence algorithms, PSO and CS outperformed the conventional algorithm by 85% and 89%, respectively. However, in terms of the overall assessment, model order 4 by the CS algorithm was selected to be the ideal model in representing the dynamic modelling of the system since it had the lowest MSE value, which fell inside the 95% confidence threshold, indicating unbiasedness and stability. © The Authors 2023. Published by University Malaysia Pahang Publishing. This is an open access article under the CC BY-NC 4.0 license
Universiti Malaysia Pahang
22298649
English
Article
All Open Access; Gold Open Access
author Jamali A.; Hassan M.H.; Lidyana R.; Hadi M.S.
spellingShingle Jamali A.; Hassan M.H.; Lidyana R.; Hadi M.S.
Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure
author_facet Jamali A.; Hassan M.H.; Lidyana R.; Hadi M.S.
author_sort Jamali A.; Hassan M.H.; Lidyana R.; Hadi M.S.
title Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure
title_short Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure
title_full Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure
title_fullStr Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure
title_full_unstemmed Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure
title_sort Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure
publishDate 2023
container_title International Journal of Automotive and Mechanical Engineering
container_volume 20
container_issue 3
doi_str_mv 10.15282/ijame.20.3.2023.01.0815
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174965341&doi=10.15282%2fijame.20.3.2023.01.0815&partnerID=40&md5=02af921d273a60bfc3257e08a104d491
description This paper focused on modelling of a gradient flexible plate system utilizing an evolutionary algorithm, namely particle swarm optimization (PSO) and cuckoo search (CS) algorithm. A square aluminium plate experimental rig with a gradient of 30° and all edges clamped were designed and fabricated to acquire input-output vibration data experimentally. This input-output data was then applied in a system identification method, which used an evolutionary algorithm with a linear autoregressive with exogenous (ARX) model structure to generate a dynamic model of the system. The obtained results were then compared with the conventional method that is recursive least square (RLS). The developed models were evaluated based on the lowest mean square error (MSE), within the 95% confidence level of both auto and cross-correlation tests as well as high stability in the pole-zero diagram. Investigation of results indicates that both evolutionary algorithms provide lower MSE than RLS. It is demonstrated that intelligence algorithms, PSO and CS outperformed the conventional algorithm by 85% and 89%, respectively. However, in terms of the overall assessment, model order 4 by the CS algorithm was selected to be the ideal model in representing the dynamic modelling of the system since it had the lowest MSE value, which fell inside the 95% confidence threshold, indicating unbiasedness and stability. © The Authors 2023. Published by University Malaysia Pahang Publishing. This is an open access article under the CC BY-NC 4.0 license
publisher Universiti Malaysia Pahang
issn 22298649
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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