Generalization Capability of Radial Basis Function Controller Using Random Search Method with Variable Search Length in Universal Learning Network

DOI HANDLE 被引用文献2件 オープンアクセス
  • Shao Ning
    Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University
  • Hirasawa Kotaro
    Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University
  • Ohbayashi Masanao
    Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University
  • Obayashi Masanao
  • Togo Kazuyuki
    Department of Energy Conversion Engineering, Interdisciplinary Graduate School of Engineering Sciences, Kyushu University

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In this paper, generalization capability of a Radial Basis Function controller using RasVal in Universal Learning Network was studied. RasVal is an abbreviation of Random Search with Variable Search Length and it can search for a global minimum systematically and effectively in a single framework which is not a combination of different methods. In this paper, a new method to overcome the over-fitting problem in nonlinear control systems is proposed, where the weighting coefficients of control variables in the criterion function are increased in order to obtain the generalization capability of RasVal. From simulation results of a nonlinear crane system, it has been shown that the smaller the scale of the R.B.F. controller is, the smaller the weighting coefficients of the control variables could be.

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詳細情報 詳細情報について

  • CRID
    1390009224843418752
  • NII論文ID
    110000579823
  • NII書誌ID
    AN10569524
  • DOI
    10.15017/1475361
  • ISSN
    21880891
    13423819
  • HANDLE
    2324/1475361
  • 本文言語コード
    en
  • データソース種別
    • JaLC
    • IRDB
    • CiNii Articles
  • 抄録ライセンスフラグ
    使用可

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