Finite Mixture Estimation Algorithm for Arbitrary Function Approximation

Abstract

The paper considers a new prospect of the arbitrary continuous function approximation from a limited set of input data with the REBMIX algorithm, developed for the finite mixture density estimation. Since the REBMIX estimates the unknown parameters with the unique semiparametric method, it is assumed that it could be used also for the estimation of the unknown parameters in the fields that are not directly connected to density function estimation. For the approximation of the arbitrary continuous function with the REBMIX algorithm, the required procedure is developed in the paper. The results gained by the proposed procedure and by the radial basis function network for three different datasets are compared by calculating the RMSE values between estimated and test output values. The adequacy of the proposed procedure is estimated by using both univariate and bivariate datasets. It can be concluded that with the developed procedure, the REBMIX algorithm can be applied successfully for the continuous function approximation.