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Proceedings Paper

Training data requirement for a neural network to predict aerodynamic coefficients
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Paper Abstract

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.

Paper Details

Date Published: 1 April 2003
PDF: 12 pages
Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); doi: 10.1117/12.486343
Show Author Affiliations
Rajkumar Thirumalainambi, SAIC/NASA Ames Research Ctr. (United States)
Jorge Bardina, NASA Ames Research Ctr. (United States)


Published in SPIE Proceedings Vol. 5102:
Independent Component Analyses, Wavelets, and Neural Networks
Anthony J. Bell; Mladen V. Wickerhauser; Harold H. Szu, Editor(s)

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