English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 61875/94645 (65%)
造訪人次 : 1635269      線上人數 : 9
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: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/115952

    題名: A Dynamic Hierarchical Fuzzy Neural Network for A General Continuous Function
    作者: W. Y. Wang, I.H. Li, S.C. Li, M.S. Tsai, and S.F Su
    關鍵詞: Fuzzy systems;Conferences
    日期: 2008-09-23
    上傳時間: 2019-03-13 12:10:22 (UTC+8)
    出版者: Springer
    摘要: A serious problem limiting the applicability of the fuzzy neural networks is the "curse of dimensionality", especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GAFSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GAFSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.
    關聯: International Journal of Fuzzy Systems 11(2), p.130-136
    DOI: 10.1109/FUZZY.2008.4630543
    顯示於類別:[機械與機電工程學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數
    A dynamic hierarchical fuzzy neural network for a general continuous function.pdf584KbAdobe PDF0檢視/開啟



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