Improved Boosting Model for Unsteady Nonlinear Aerodynamics based on Computational Intelligence

Boxu Zhao, Guiming Luo and Jihong Zhu
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Boxu Zhao: School of Software, Tsinghua University, Beijing, China
Guiming Luo: School of Software, Tsinghua University, Beijing, China
Jihong Zhu: Department of Computer Science and Technology, Tsinghua University, Beijing, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2017, vol. 11, issue 1, 46-59

Abstract: The large-amplitude-oscillation experiment was carried out with two levels of freedom to provide data. Based on the wind tunnel data, polynomial regression, least-square support vector machines and radial basis function neural networks are studied and compared in this paper. An improved model was also developed in this work for unsteady nonlinear aerodynamics on the basis of standard boosting approach. The results on the wind tunnel data show that the predictions of the method are almost consistent with the actual data, thus demonstrating that these methods can model highly nonlinear aerodynamics. The results also indicate that improved boosting model has better accuracy than the other methods.

Date: 2017
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