摘要
提出了一种重构功能函数的新方法。该方法采用模式识别中的最小错分样本数准则以实现对样本点较好的分类效果,从而得到功能函数的一个较好逼近。然后通过迭代算法修正每步得到的设计点以实现在真实设计点附近较高精度地逼近真实功能函数。它克服了一般响应面方法需用到功能函数值的不足。数值算例表明:该方法具有较高的精度且所需样本数量较少。
A method to restructure the performance function is proposed. Based on the minimum misclassified sampling rule in pattern recognition it can achieve good effects to classify samples, then a good approximation to the performance function can be obtained. An iterative algorithm is used to approximate the performance function accurately around the true design point through modifcation to the solution of design points in each step. It overcomes the disadvantage that the general response surface method needs the performance function values. Numerical examples show that the method is of high precision with fewer samples.
出处
《工程力学》
EI
CSCD
北大核心
2008年第8期53-57,共5页
Engineering Mechanics
基金
长沙理工大学基金项目(1004157)
关键词
结构工程
可靠度
模式识别
分类器
线性规划
structural engineering
reliability
pattern recognition
classifier
linear programming
