A multi-criteria adaptive sequential sampling method for radial basis function

Haiyang Hu, Zhansi Jiang, Yanxue Wang, Shuilong He
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Abstract

A multi-criteria adaptive sequential sampling method is proposed for radial basis function metamodel and a new global approximation method is developed in this paper. In this new sampling method, objective, curvature and distance are considered as sampling criteria. With the three criteria, it guarantees that the entire domain will be covered by samples, and more sampling points will be gathered in the peak and valley regions, which is useful for enhance accuracy and efficiency of approximation model. Intensive testing shows that the efficiency of method and accuracy of metamodel are satisfactory by this new global approximation method.
径向基函数的多准则自适应序贯采样方法
针对径向基函数元模型提出了一种多准则自适应序贯采样方法,并提出了一种新的全局逼近方法。该方法将物镜、曲率和距离作为采样准则。这三个准则保证了样本覆盖整个域,在峰谷区域收集到更多的采样点,有助于提高近似模型的精度和效率。大量的测试表明,该方法的效率和元模型的精度令人满意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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