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

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)

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Abstract

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.

Item Type:	Article (Journal)
Additional Information:	5078/4564
Uncontrolled Keywords:	Aerodynamic derivatives cross wind; Vehicle stability; PCA; Principal component analysis; Neural network.
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ170 Mechanics applied to machinery. Dynamics
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User:	Prof.Dr. Waleed Fekry Faris
Date Deposited:	23 Sep 2011 09:29
Last Modified:	22 Nov 2011 16:51
URI:	http://irep.iium.edu.my/id/eprint/4564

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