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

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

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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:	4694/49833
Uncontrolled Keywords:	aerodynamic derivatives cross wind; vehicle stability; PCA; principal component analysis; neural network.
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TJ Mechanical engineering and machinery > TJ181 Mechanical movements T Technology > TJ Mechanical engineering and machinery > TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering
Depositing User:	Prof.Dr. Waleed Fekry Faris
Date Deposited:	06 Apr 2016 17:05
Last Modified:	18 Jul 2016 08:33
URI:	http://irep.iium.edu.my/id/eprint/49833

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