Radial Basis Function Network in Reproducing Kernel Hilbert Space

DOI HANDLE Web Site オープンアクセス
  • Dachapak Chooleewan
    Department of Electrical and Electronic Systems Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University : Graduate Student
  • 金江 春植
    九州大学大学院システム情報科学研究院電気電子システム工学部門
  • 楊 子江
    九州大学大学院システム情報科学研究院電気電子システム工学部門
  • 和田 清
    九州大学大学院システム情報科学研究院電気電子システム工学部門

この論文をさがす

抄録

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.

収録刊行物

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ