| 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,2013,30(4):485~490 |
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| 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 |
| 投稿时间：2012-03-19 修订日期：2012-07-06 |
| DOI：10.7511/jslx201304005 |
| 中文关键词: 可靠性 优化 支持向量机 |
| 英文关键词:reliability optimization support vector machines |
| 基金项目:中国博士后基金(2011M500919)资助项目. |
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| 摘要点击次数: 2941 |
| 全文下载次数: 1558 |
| 中文摘要: |
| 在工程设计中,可靠性优化设计通常计算量较大或精度不够。本文提出了一种基于支持向量机(Support Vector Machine)和MPP(Most Probable Point)的可靠性分析方法。用SVM在MPP处替代原极限状态函数,并利用极限状态函数的梯度信息,使SVM模型穿过MPP并与原函数相切,再基于SVM采用重要抽样法计算失效概率。然后,将SORA(Sequential Optimization and Reliability Assessment)与基于SVM的可靠性分析方法相集成,将传统的双循环可靠性优化算法解耦为单循环,并通过基于SVM的可靠性分析方法修正了SORA中由于线性近似带来的误差,保证了最优设计点处可靠性分析的精度。算例证明,该方法在处理非线性问题时具有精确度高和计算量适度的特点。 |
| 英文摘要: |
| Traditional 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 limit-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 calculate 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. |
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