Supervised training technique for radial basis function neural networks
A novel supervised technique for training classifiers based on radial basis function (RBF) neural networks is presented. Unlike traditional techniques, this considers the class-membership of training samples to select the centres and widths of the kernel functions associated with the hidden units of an RBF network. Experiments carried out to solve an industrial visual inspection problem confirmed the effectiveness of the proposed technique.
