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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). 59 - 71. ISSN 17456436

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Official URL: http://dx.doi.org/10.1504/IJVSMT.2010.033731

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
Uncontrolled Keywords:Aerodynamic derivatives cross wind, neural network, PCA Principal component analysis, vehicle stability
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Mechanical Engineering
ID Code:22840
Deposited By: Kamariah Mohamed Jong
Deposited On:13 Sep 2017 15:44
Last Modified:13 Sep 2017 15:48

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