Using RBF reduced by QLP decomposition for Probability Density Estimation

This paper is intender to be a simple example illustrating some of the capabilities of Radial basis function by pruning with QLP decomposition. The applicability of the radial basis function (RBF) type function of artificial neural networks (ANNS) approach for re-estimate the Box, Traingle, Epanechnikov and Normal densities. We propose an application of QLP decomposition model to reduce to the class of RBF neural models for improving performance in contexts of density estimate. Has been found in the QLP that such a coupling leads to more precise extraction of the relevant information, even when using it in a heuristic way. This paper is concerned with reestimation these four densities estimated by pruning a Radial Basis Function network using pivoted QLP decomposition. For comparison all RBF type functions with the same Gaussian mixture model as the sample data is superimposed on the plot. This application tool can be used to identify the density estimate from empirical data where presents many type density estimative. The QLP methods proves efficient for reducing the network size by pruning hidden nodes, resulting is a parsimonious model which identify RBF type multiquadric to re-estimate kernel function Box and Normal distributions. Key-Words: -Density estimate, RBF , Pivote QLP Decomposition, adjust function, Bayesian Information Criterion