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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
Language:English
Published: Inderscience Enterprises Ltd. 2010
Subjects:
TJ170 Mechanics applied to machinery. Dynamics
Online Access:http://irep.iium.edu.my/4564/4/Aerodynamic_derivatives_identification_for_ground.pdf
http://irep.iium.edu.my/4564/
http://www.inderscience.com/search/index.php?action=record&rec_id=33731
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spelling my.iium.irep.45642011-11-22T08:51:23Z http://irep.iium.edu.my/4564/ Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA Ramli, Nabilah Jamaluddin, Hishamuddin Mansor, Shuhaimi Faris, Waleed Fekry TJ170 Mechanics applied to machinery. Dynamics 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 Enterprises Ltd. 2010 Article REM application/pdf en http://irep.iium.edu.my/4564/4/Aerodynamic_derivatives_identification_for_ground.pdf 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). pp. 59-71. ISSN 1745-6444 (O), 1745-6436 (P) http://www.inderscience.com/search/index.php?action=record&rec_id=33731 10.1504/IJVSMT.2010.033731
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TJ170 Mechanics applied to machinery. Dynamics
spellingShingle TJ170 Mechanics applied to machinery. Dynamics
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 Enterprises Ltd.
publishDate 2010
url http://irep.iium.edu.my/4564/4/Aerodynamic_derivatives_identification_for_ground.pdf
http://irep.iium.edu.my/4564/
http://www.inderscience.com/search/index.php?action=record&rec_id=33731
_version_ 1643605364760903680
score 13.160551

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