计算机试验下Kriging模型选择的比较
Comparison of Model Selection for Kriging Model in Computer Experiments
DOI: 10.12677/AAM.2021.103076, PDF,  被引量   
作者: 李 涵, 王 晓, 李新民：青岛大学数学与统计学院，山东 青岛；赵建昕：海军潜艇学院基础部，山东 青岛
关键词: Kriging模型；模型选择；Elastic Net；Lasso；Kriging Model； Model Selection； Elastic Net； Lasso
摘要: Kriging模型是计算机试验的一种常用模型，因具有良好的非线性拟合能力而被广泛使用。本文在一般Kriging模型相关研究的基础上，研究了Kriging模型的变量选择问题，并给出了Elastic Net变量选择方法。与Lasso和adaptive Lasso相比，数值模拟表明Elastic Net变量选择方法能够提高拟合模型的准确性和稳定性。
Abstract: Kriging model is a common model of computer experiment, which is widely used because of its good nonlinear fitting ability. Based on the research of Kriging model, this paper studies the variable selection of universal Kriging model, and gives the variable selection method of Elastic Net. Compared with Lasso and adaptive Lasso, numerical simulation shows that Elastic Net variable selection method can improve the accuracy and stability of the fitting model.
文章引用：李涵, 赵建昕, 王晓, 李新民. 计算机试验下Kriging模型选择的比较[J]. 应用数学进展, 2021, 10(3): 694-700. https://doi.org/10.12677/AAM.2021.103076

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