The Application of MARS on Crashworthiness Improvement
Surrogate model produced by using multivariate adaptive regression spline (MARS) approach takes the form of an expansion in a set of basis functions which are selected from data. This approach is especially useful in the case of having no advance understanding of the parametric model. As for MARS procedure, the adaptive adjustment is used frequently to best fit the data optimally. In this paper, the adaptive adjustment is realized by using Matlab programming language and provides support for MARS procedure. Taking crashworthiness improvement for example, the program is applied to produce surrogate model for peak acceleration, and then the optimization is carried out based on this model. The results indicate that the surrogate model constructed by MARS approach can predict peak acceleration precisely and therefore can provide instruct for crashworthiness improvement.
MARS Surrogate model Adaptive adjustment Peak acceleration
Yunkai Gao Fang Sun
School of Automotive, Tongji University, Shanghai, China
国际会议
沈阳
英文
384-388
2010-07-28（万方平台首次上网日期，不代表论文的发表时间）