TY - GEN
T1 - RBF neural network ensemble method and aerodynamic optimization
AU - Su, Wei
AU - Gao, Zhenghong
AU - Zuo, Yingtao
PY - 2013
Y1 - 2013
N2 - To reduce the expensive computational cost in optimization, surrogate model is suggested to evaluate objective function. However, for its prediction error surrogate model is likely to mislead the search in optimization. To avoid this danger, on one hand, improving prediction ability of surrogate model is necessary. In this paper, neural network ensemble method is used to reduce prediction error of single RBF neural network. On the other hand, effective management of optimization algorithm and surrogate model is needed. In this paper, MADS optimization algorithm and RBF neural network ensemble are integrated effectively. The test cases show that this method is efficient and effective for aerodynamic shape optimization.
AB - To reduce the expensive computational cost in optimization, surrogate model is suggested to evaluate objective function. However, for its prediction error surrogate model is likely to mislead the search in optimization. To avoid this danger, on one hand, improving prediction ability of surrogate model is necessary. In this paper, neural network ensemble method is used to reduce prediction error of single RBF neural network. On the other hand, effective management of optimization algorithm and surrogate model is needed. In this paper, MADS optimization algorithm and RBF neural network ensemble are integrated effectively. The test cases show that this method is efficient and effective for aerodynamic shape optimization.
UR - https://www.scopus.com/pages/publications/84904695963
M3 - 会议稿件
AN - SCOPUS:84904695963
SN - 9781629939094
T3 - Proceedings of the International Astronautical Congress, IAC
SP - 1478
EP - 1489
BT - 64th International Astronautical Congress 2013, IAC 2013
PB - International Astronautical Federation, IAF
T2 - 64th International Astronautical Congress 2013, IAC 2013
Y2 - 23 September 2013 through 27 September 2013
ER -