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Improved Boosting Model for Unsteady Nonlinear Aerodynamics based on Computational Intelligence

Author

Listed:
  • 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)

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.

Suggested Citation

  • Boxu Zhao & Guiming Luo & Jihong Zhu, 2017. "Improved Boosting Model for Unsteady Nonlinear Aerodynamics based on Computational Intelligence," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 11(1), pages 46-59, January.
    • Handle: RePEc:igg:jcini0:v:11:y:2017:i:1:p:46-59
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