  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.
