计算力学学报Issue(4)：485-490,6.DOI:10.7511/jslx201304005
基于支持向量机的序列可靠性优化方法
Sequential reliability-based optimization with support vector machines
摘要
Abstract
T raditional reliability-based design optimization (RBDO ) is either computational intensive or not accurate enough .In this work ,a new RBDO method based on Support Vector Machines (SVM ) is proposed .For reliability analysis ,SVM is used to create a surrogate model of the limit-state function at the Most Probable Point (MPP) .The uniqueness of the new method is the use of the gradient of the lim-it-state function at the MPP .This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP .Then Importance Sampling (IS) is used to cal-culate the probability of failure based on the surrogate model .This treatment significantly improves the accuracy of reliability analysis .For optimization ,the Sequential Optimization and Reliability Assessment (SORA) is employed ,which decouples deterministic optimization from the SVM reliability analysis .The decoupling makes RBDO more efficient .The two examples show that the new method is more accurate with a moderately increased computational cost .关键词
可靠性/优化/支持向量机Key words
reliability/optimization/support vector machines分类
通用工业技术引用本文复制引用
王宇,余雄庆,杜小平..基于支持向量机的序列可靠性优化方法[J].计算力学学报,2013,(4):485-490,6.基金项目
中国博士后基金（2011M500919）资助项目． （）
