Large Scattered Data Fitting Based on Radial Basis Functions

Solving large radial basis function (RBF) interpolation problem with non-customized methods is computationally expensive and the matrices that occur are typically badly conditioned. In order to avoid these difficulties, we present a fitting based on radial basis functions satisfying side conditions by least squares, although compared with interpolation the method loses some accuracy, it reduces the computational cost largely. Since the fitting accuracy and the non-singularity of coefficient matrix in normal equation are relevant to the uniformity of chosen centers of the fitted RBF, we present a choice method of uniform centers. Numerical results confirm the fitting efficiency.