  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.
