摘要
在多学科设计优化中常利用近似模型降低计算量,但传统的近似模型生成方法难以解决近似精度与训练集规模间的矛盾。提出了一种迭代更新的近似模型生成方法,在建立初步的近似模型后,根据梯度、波动等特征产生补充训练集,用于更新近似模型,从而增强在试验设计中选取训练集的适应性和灵活性,在近似精度与训练集规模之间取得更好的折衷。算例表明该方法的优越性,能有效地应用于实际工程中。
Approximate models is widely used to reduce computational cost in multidisciplinary design optimization, but it is hard for traditional methods of generating approximate model to solve the conflict between the approximate precision and size of training set. An iteratively updating method of generating approximate model is proposed to solve this problem. In this method, after initial approximate model is constructed, complementary training set is generated according to such characteristics as gradient and fluctuation, and then the training set is used to update the approximate model. Therefore, the adaptability and flexibility of selecting training set in design of experiment can be enhanced, and better tradeoff between approximate precision and size of training set can be achieved. Examples show that this new method is superior to traditional ones, and it can be applied to practical engineering problems effectively.
出处
《航天控制》
CSCD
北大核心
2009年第5期58-64,共7页
Aerospace Control
基金
国家973项目(5132004)
关键词
近似模型
试验设计
近似梯度
多学科设计优化
Approximate model
Design of experiment
Approximate gradient
Multidisciplinary design optimization