Applying least squares support vector machines to the airframe wing-box structural design cost estimation

作者：Deng S; Yeh Tsung Han^*

来源：Expert Systems with Applications, 2010, 37(12): 8417-8423.

DOI：10.1016/j.eswa.2010.05.038

• Airframe structure
• Cost estimation
• Least squares support vector machines
• Back-propagation neural networks
• Response surface methodology

This research used the least squares support vector machines (LS-SVM) method to estimate the project design cost of an airframe wing-box structure. We also compared the estimation performance using back-propagation neural networks (BPN) and statistical response surface methodology (RSM). The solution mechanism of the LS-SVM involved a simultaneous searched for the maximal margin as the target, taking into account the error calculated during training phase to determine the estimation problem models. Two case studies involving the wing-box structure was investigated the separate structural parts case and the mixed structural parts case. The test results verified the feasibility of using the LS-SVM as well as its ability to make accurate estimations.

• 出版日期2010-12
• 单位中国人民解放军国防大学