縦流式道路トンネルにおけるオンライン学習形換気制御 Online-learning Type of Ventilation Control Systems in Longitudinal-flow Road Tunnels

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The road-traffic flow causes tunnel vetilation load. It changes seasonally and year by year and so the tuning of tunnel ventilation systems takes a lot of time. With these points as background, this paper proposes a online-learning type of ventilation control system. This system uses two types of neural networks. One is NNVI (Neural Network for VI control) which learns the change of VI (Visibility Index) successively. The other is NNAF (Neural Network for Air-Flow control) which learns the change of air-flow successively. NNVI also outputs control instructions to keep VI higher than a permissible level in parallel with the above learning. NNAF also outputs the ventilation control instructions to keep air-flow higher than a permissible level so that air-flow does not turn over. This paper describes the construction of the proposed ventilation control system and the automatic learning method. And using the dynamic digital simulator it shows the estimation results of the control performance based on the road-traffic flow data.

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 117(8), 970-979, 1997-08 

    The Institute of Electrical Engineers of Japan

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各種コード

  • NII論文ID(NAID)
    10002724985
  • 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
    4267306
  • 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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