Radial Basis Function Network in Reproducing Kernel Hilbert Space

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概要

• 論文の詳細を見る

• 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.

• 九州大学の論文

著者

• KANAE Shunshoku

  Graduate school of information science and electrical engineering, Kyushu University

• Yang Z‐j

  Graduate School Of Information Science And Electrical Engineering Kyushu University

• Yang Zi‐jiang

  Kyushu Univ.

• Kanae Shunshoku

  Kyushu Univ.

• Kanae Shunshoku

  Graduate School Of Information Science And Electrical Engineering Kyushu University

• DACHAPAK Chooleewan

  Department of Electrical and Electronic Systems Engineering, Graduate School of Information Science

• KANAE Shunshoku

  Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Elec

• YANG Zi-Jiang

  Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Elec

• Yang Zi-jiang

  Department Of Electrical And Electronic Systems Engineering Graduate School Of Information Science A

• Dachapak Chooleewan

  Department Of Electrical And Electronic Systems Engineering Graduate School Of Information Science A

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