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...

Full description

Bibliographic Details
Main Authors:	Ramli, Nabilah, Jamaluddin, Hishamuddin, Mansor, Shuhaimi B., Faris, Waleed Fekry
Format:	Article
Language:	English
Published:	Inderscience Enterprises Ltd. 2010
Subjects:	TJ Mechanical engineering and machinery TJ181 Mechanical movements TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General)
Online Access:	http://irep.iium.edu.my/49833/ http://irep.iium.edu.my/49833/ http://irep.iium.edu.my/49833/ http://irep.iium.edu.my/49833/1/Aerodynamic_derivatives_identification_for_ground_vehicles_in_crosswind_using_neural_network_and_PCA.pdf

id	iium-49833
recordtype	eprints
spelling	iium-498332016-07-18T00:33:51Z http://irep.iium.edu.my/49833/ Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA Ramli, Nabilah Jamaluddin, Hishamuddin Mansor, Shuhaimi B. Faris, Waleed Fekry TJ Mechanical engineering and machinery TJ181 Mechanical movements TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General) 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 PeerReviewed application/pdf en http://irep.iium.edu.my/49833/1/Aerodynamic_derivatives_identification_for_ground_vehicles_in_crosswind_using_neural_network_and_PCA.pdf Ramli, Nabilah and Jamaluddin, Hishamuddin and Mansor, Shuhaimi B. 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 1745-6436 E-ISSN 1745-6444 http://dx.doi.org/10.1504/IJVSMT.2010.033731 doi:10.1504/IJVSMT.2010.033731
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery TJ181 Mechanical movements TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General)
spellingShingle	TJ Mechanical engineering and machinery TJ181 Mechanical movements TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General) Ramli, Nabilah Jamaluddin, Hishamuddin Mansor, Shuhaimi B. 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 B. Faris, Waleed Fekry
author_facet	Ramli, Nabilah Jamaluddin, Hishamuddin Mansor, Shuhaimi B. 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/49833/ http://irep.iium.edu.my/49833/ http://irep.iium.edu.my/49833/ http://irep.iium.edu.my/49833/1/Aerodynamic_derivatives_identification_for_ground_vehicles_in_crosswind_using_neural_network_and_PCA.pdf
first_indexed	2023-09-18T21:10:24Z
last_indexed	2023-09-18T21:10:24Z
_version_	1777411214099349504

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

Similar Items