A New Radial Basis Function Approximation with Reproduction
Graphics
2018-04-19 v1
Abstract
Approximation of scattered geometric 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 method is useful for a higher dimension d>=2, because the other methods require a conversion of a scattered dataset to a semi-regular mesh using some tessellation techniques, which is computationally expensive. The RBF approximation is non-separable, as it is based on a distance of two points. It leads to a solution of overdetermined Linear System of Equations (LSE). In this paper a new RBF approximation method is derived and presented. The presented approach is applicable for d dimensional cases in general.
Cite
@article{arxiv.1804.06662,
title = {A New Radial Basis Function Approximation with Reproduction},
author = {Zuzana Majdisova and Vaclav Skala},
journal= {arXiv preprint arXiv:1804.06662},
year = {2018}
}