抄録
This paper presents two novel radial basis functions and their comparison. Both radial basis functions are based on an idea of Support Vector Machine (SVM) by mapping data into a high dimensional feature space, which is known as Reproducing Kernel Hilbert Space and then performing Radial Basis Function (RBF) network in the feature space. Orthogonal Least Squares (OLS) method is employed to select a suitable set of centers (regressors) from a large set of candidates in order to obtain a sparse regression model in the feature space. The proposed method is applied to a nonlinear system identification problem by simulations.
