一般化学習ネットサークの2次微分を用いた非線形ダイナミカルシステムの外部入力変動に対するロバスト制御方式 Robust Control of a Nonlinear Dynamical System for Changes of External Inputs Using Second Order Derivative of Universal Learning Network

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As the nonlinearity and complexity of a nonlinear system increase, it may be more difficult to construct a controller by the mathematical control theory. In such cases, it is very effective to construct the controller by using Neural Network(NN), because NNs have capabilities of coping with the nonlinearity and complexity of the nonlinear systems. NN controllers are constructed through learning to minimize a criterion function under certain environments of the system. But NN contollers may not work well under a very different environment from the environment at learning stage. In other words, for example, NN controllers are usually made without considering the changes of the environment because NN controllers do not have a means to supress their influences. So, in case that the environment changes, NN controllers do not work well. In this paper a robust control system design method for the changes of the external inputs to the system is discussed using second order derivatives of Universal Learning Network.

収録刊行物

  • 電気学会論文誌. D, 産業応用部門誌 = The transactions of the Institute of Electrical Engineers of Japan. D, A publication of Industry Applications Society  

    電気学会論文誌. D, 産業応用部門誌 = The transactions of the Institute of Electrical Engineers of Japan. D, A publication of Industry Applications Society 118(3), 300-307, 1998-03 

    The Institute of Electrical Engineers of Japan

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各種コード

  • NII論文ID(NAID)
    10002726424
  • NII書誌ID(NCID)
    AN10012320
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    09136339
  • NDL 記事登録ID
    4413260
  • NDL 雑誌分類
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL 請求記号
    Z16-1608
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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