华中科技大学学报：自然科学版2012，Vol.40Issue(4)：29-32,4.
基于支持向量回归机的结构非概率可靠性分析
Structural non-probabilistic reliability analysis using support vector regression
摘要
Abstract
The regression technology of support vector machine（SVM） was introduced to analyze the non-probabilistic reliability of structures with implicit limit state function.Based on the fragment description model of unascertained information,the training data sampling method was proposed.The sample data in basic variable range was transformed to that in norm interval variable scale,and the dimensions of training samples were unified.So the stability of SVM could be assured.The algorithm was offered,and the training,test and prediction sample could be directly drawn in norm interval scales.So the sample drawing and Monte Carlo simulation became easier to realize.The accuracy and feasibility of this methodology were proved through two given examples.The problem of structural non-probabilistic reliability with implicit limit state function can be solved using this technique,and which is easy to use.关键词
可靠性分析/结构可靠性/支持向量回归机/蒙特卡罗/隐式极限状态/非概率可靠性Key words
reliability anaylsis/structural reliability/support vector regression(SVR)/Monte Carlo/implicit limit state/non-probabilistic reliability引用本文复制引用
孙文彩,杨自春,李昆锋..基于支持向量回归机的结构非概率可靠性分析[J].华中科技大学学报：自然科学版,2012,40(4):29-32,4.基金项目
总装备部武器装备预研项目 （）
教育部新世纪优秀人才支持计划资助项目 （）
