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Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA

Principal component analysis (PCA) is employed in this study to reduce the size of the neural network input node. Neural network is used to identify the ground vehicle aerodynamic derivatives based on a recorded simple harmonic motion of a ground vehicle model. The study involves the identification...

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Main Authors: Ramli, Nabilah, Jamaluddin, Hishamuddin, Mansor, Shuhaimi, Faris, Waleed Fekry
Format: Article
Published: Inderscience Publishers 2010
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/22840/
http://dx.doi.org/10.1504/IJVSMT.2010.033731
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spelling my.utm.228402017-09-13T07:48:26Z http://eprints.utm.my/id/eprint/22840/ Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA Ramli, Nabilah Jamaluddin, Hishamuddin Mansor, Shuhaimi Faris, Waleed Fekry TJ Mechanical engineering and machinery Principal component analysis (PCA) is employed in this study to reduce the size of the neural network input node. Neural network is used to identify the ground vehicle aerodynamic derivatives based on a recorded simple harmonic motion of a ground vehicle model. The study involves the identification using neural network with and without the input optimisation by PCA. Both studies are compared with the identification results from a conventional method, and it is shown that the neural network can approximate functions based on principal components extracted as well as a full-size neural network can. Inderscience Publishers 2010-06 Article PeerReviewed Ramli, Nabilah and Jamaluddin, Hishamuddin and Mansor, Shuhaimi and Faris, Waleed Fekry (2010) Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA. International Journal of Vehicle Systems Modelling and Testing, 5 (1). 59 - 71. ISSN 17456436 http://dx.doi.org/10.1504/IJVSMT.2010.033731 DOI: 10.1504/IJVSMT.2010.033731
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Ramli, Nabilah
Jamaluddin, Hishamuddin
Mansor, Shuhaimi
Faris, Waleed Fekry
Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA
description Principal component analysis (PCA) is employed in this study to reduce the size of the neural network input node. Neural network is used to identify the ground vehicle aerodynamic derivatives based on a recorded simple harmonic motion of a ground vehicle model. The study involves the identification using neural network with and without the input optimisation by PCA. Both studies are compared with the identification results from a conventional method, and it is shown that the neural network can approximate functions based on principal components extracted as well as a full-size neural network can.
format Article
author Ramli, Nabilah
Jamaluddin, Hishamuddin
Mansor, Shuhaimi
Faris, Waleed Fekry
author_facet Ramli, Nabilah
Jamaluddin, Hishamuddin
Mansor, Shuhaimi
Faris, Waleed Fekry
author_sort Ramli, Nabilah
title Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA
title_short Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA
title_full Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA
title_fullStr Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA
title_full_unstemmed Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA
title_sort aerodynamic derivatives identification for ground vehicles in crosswind using neural network and pca
publisher Inderscience Publishers
publishDate 2010
url http://eprints.utm.my/id/eprint/22840/
http://dx.doi.org/10.1504/IJVSMT.2010.033731
_version_ 1643647412191887360
score 13.160551

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