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We proposed an algorithm to construct ensemble radical basis function neural networks for regression estimation. Taking full advantage of the characteristic of radial basis function, we calculated groups of approximate basis in Reproducing Kernel Hilbert Space (RKHS). The approximate basis could be used to represent all the samples by the way of linear combination. By this way, the weak learners of radial basis function neural network were built. But it was proved that the weak learners were not accurate enough. In order to get accurate and stable learning machine with better generalization ability, we proposed the Ensemble Radical Basis Function Neural Networks (ERBFNNs). Employing the sinc function, the proposed ERBFNNs have shown exciting outcomes as have come out at the end of the paper. Introduction The problem of empirical data modeling is germane to many engineering applications. For this purpose, Broomhead and Lowe developed RBFNNs in 1988 [1] . Consider the standard supervised learning problem. In a regression problem, one is supplied with a set of data points 1 1 {( , ), ,( , )} N y y R    d N D x x R that are sampled from an unknown function, where , 1, , i N  i x are input points and , 1, , , y i N  i the corresponding output points. The goal is to find a fit to the data points such that an approximation to the unknown function is obtained. A RBFNN is an approximation of the form:

Ensemble Radical Basis Function Neural Networks for Regression Based on Statistical Learning Theory