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arXiv 1806.07705math.NAcs.NA
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径向基函数逼近:比较与应用

Radial Basis Function Approximations: Comparison and Applications

Zuzana Majdisova, Vaclav Skala

AI总结:

本文比较了多种径向基函数逼近方法,探讨其在高维数据中的稳定性和精度,提出的方法具有更低的内存需求和更优的逼近质量。

摘要:

散数据的近似通常是许多工程问题中的任务。径向基函数(RBF)逼近适用于大散数据集,在d维空间中尤为有效。该方法在高维d>2时特别有用,因为其他方法需要将散数据转换为有序数据集(即通过某些剖分技术获得半规则网格),这计算成本高。RBF逼近是非分离的,因为它基于两点间距离。该方法导致线性方程组(LSE)Ac=h的解。本文简要介绍了几种RBF逼近方法,并就其稳定性和精度进行了比较。所提出的RBF逼近方法具有更低的内存需求和更好的逼近质量。

英文摘要:

Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for large scattered (unordered) datasets in d-dimensional space. This approach is useful for a higher dimension d>2, because the other methods require the conversion of a scattered dataset to an ordered dataset (i.e. a semi-regular mesh is obtained by using some tessellation techniques), which is computationally expensive. The RBF approximation is non-separable, as it is based on the distance between two points. This method leads to a solution of Linear System of Equations (LSE) Ac=h. In this paper several RBF approximation methods are briefly introduced and a comparison of those is made with respect to the stability and accuracy of computation. The proposed RBF approximation offers lower memory requirements and better quality of approximation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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