Function approximation from noisy data by an incremental RBF network

摘要

An incremental radial basis function (RBF) network for function approximation from noisy data is proposed. Hidden Gaussian nodes are iteratively added in the training process. This adding procedure is stopped only when the residual error is under a fixed threshold related to the noise level. The results extend to noisy data Esposito et al.'s strategy.