A New Radial Basis Function Approximation with Reproduction

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 semiregular mesh using some tessellation techniques, which is computationally expensive. The RBF approximation is nonseparable, 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.