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Width optimization of the Gaussian kernels in Radial Basis Function Networks

Radial basis function networks are usually trained according to a three-stage procedure. In the literature, many papers are devoted to the estimation of the position of Gaussian kernels, as well as the computation of the weights. Meanwhile, very few focus on the estimation of the kernel widths. In this paper, first, we develop a heuristic to optimize the widths in order to improve the generalization process. Subsequently, we validate our approach on several theoretical and real-life approximation problems.