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A Sequential Optimization Sampling Method for Metamodels with Radial Basis Functions

机译:具有径向基函数的元模型的顺序优化抽样方法

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摘要

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is strongly affected by the sampling methods. In this paper, a new sequential optimization sampling method is proposed. Based on the new sampling method, metamodels can be constructed repeatedly through the addition of sampling points, namely, extrema points of metamodels and minimum points of density function. Afterwards, the more accurate metamodels would be constructed by the procedure above. The validity and effectiveness of proposed sampling method are examined by studying typical numerical examples.
机译:元模型已广泛用于工程设计中,以促进复杂系统的分析和优化,这些复杂系统涉及计算上昂贵的仿真程序。元模型的准确性受采样方法的强烈影响。本文提出了一种新的顺序优化抽样方法。基于新的采样方法,可以通过添加采样点(即元模型的极值点和密度函数的最小点)来重复构建元模型。之后,将通过上述过程构建更准确的元模型。通过研究典型的数值例子来检验所提出的抽样方法的有效性和有效性。

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