English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 54102/88902 (61%)
造訪人次 : 10552159      線上人數 : 19
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/77240


    題名: Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
    作者: Chiang, Y.M.;Chang, L.C.;Tsai, M.J.;Wang, Y.F.;Chang, F.J.
    貢獻者: 淡江大學水資源及環境工程學系
    日期: 2011
    上傳時間: 2012-06-14 09:11:30 (UTC+8)
    出版者: Goettingen: Copernicus GmbH
    摘要: Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.
    關聯: Hydrology and Earth System Sciences 15(1), pp.185-196
    DOI: 10.5194/hess-15-185-2011
    顯示於類別:[水資源及環境工程學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    1027-5606_15(1)p185-196.pdf608KbAdobe PDF220檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋