Radial Basis Function Network in Reproducing Kernel Hilbert Space

DOI HANDLE Web Site Open Access
  • Dachapak Chooleewan
    Department of Electrical and Electronic Systems Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University : Graduate Student
  • Kanae Shunshoku
    Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Electrical Engineeringg, Kyushu University
  • Yang Zi-Jiang
    Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University
  • Wada Kiyoshi
    Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University

Search this article

Abstract

The present study employs an idea of mapping data into a high dimensional feature space which is known as Reproducing Kernel Hilbert Space (RKHS), then performs Radial Basis Function (RBF) network in the feature space where the new basis function will be obtained and finally, 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 employed to the simple scalar function estimation problems and nonlinear system identification problem by simulations.

Journal

Keywords

Details 詳細情報について

Report a problem

Back to top