Optimal Design of Radial Basis Function using Taguchi Method

Development of the radial basis function networks (RBFNs) can be divided into two stages. First, learning the centres and widths of the radial basis function and next, learning the connection weight. The performance of the RBFN depends entirely on these two learning algorithms. Hence, in this paper, we proposed a new algorithm wherein the centres and widths of the radial basis function in regression problem are selected using the Taguchi method. Some experiments of function estimation are conducted in order to illustrate the performance of the proposed algorithm.