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| Surrogate-based reliability analysis by support vector machine |
| Received:November 17, 2009 Revised:April 26, 2010 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx201105005 |
| KeyWord:reliability support vector machine regression algorithm classification algorithm genetic algorithm |
| Author | Institution |
| 李刚 |
大连理工大学 工程力学系 工业装备结果分析国家重点实验室,大连 |
| 刘志强 |
大连理工大学 工程力学系 工业装备结果分析国家重点实验室,大连 |
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| Abstract: |
| This paper establishes the surrogate-based reliability analysis method for the implicit performance functions using the support vector regression algorithm and classification algorithm, in which Monte Carlo simulation method is integrated with the Latin hypercube sampling technique. The effects of the related parameters on the SVM performance are discussed and the genetic algorithm is used to optimize the parameters to provide a rational selection of SVM model. The efficiency of the SVM model with different sampling size is discussed, which testifies the good performance of SVM model with small sampling size. Finally, the numerical examples indicate the feasibility and efficiency of the proposed method. |