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Bi-model RBDO process based on constructing SVM model by using adaptive support vector clamping method

机译:基于使用自适应支持向量夹紧方法构建SVM模型的双模型RBDO处理

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Kriging model is an efficient method to solve reliability-based design optimization problem with black box constraints. However, the estimation process of the Kriging model must be based on all sample points, so there still needs a large evaluate time, especially when dealing with high dimensional constraints. To overcome this difficulty, a bi-model RBDO process is proposed in this paper. In the first stage, accurate Kriging model of the constraint is constructed. In the second stage, accurate SVM model is constructed based on the Kriging model by using adaptive support vector clamping method. And the optimal design point of the RBDO problem is calculated based on this SVM model. The results show that the proposed approach is more efficient with necessary accuracy when solving the RBDO problem.
机译:Kriging Model是一种有效的方法,可以使用黑盒子约束解决基于可靠性的设计优化问题。然而,Kriging模型的估计过程必须基于所有采样点,因此仍需要大的评估时间,特别是在处理高维约束时。为了克服这种困难,本文提出了双模型RBDO处理。在第一阶段,构建了约束的准确克里格化模型。在第二阶段,通过使用自适应支持向量夹紧方法基于Kriging模型构造精确的SVM模型。基于该SVM模型计算RBDO问题的最佳设计点。结果表明,当解决RBDO问题时,所提出的方法在必要的准确性方面更有效。

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