English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 56557/90363 (63%)
造訪人次 : 11843753      線上人數 : 148
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/987654321/95870


    題名: A Neuro-Fuzzy Approach to System Identification
    作者: Su, Mu-Chun;Kao, Chien-Jen
    貢獻者: 淡江大學電機工程學系
    關鍵詞: 系統識別;類神經網路;模糊理論;System Identification;Neural Network;Fuzzy Theory
    日期: 1994-12
    上傳時間: 2014-02-13 11:14:02 (UTC+8)
    摘要: In this paper, we present an innovative approach to the identification of non-linear systems. The proposed neuro-fuzzy system identifier employs a hybrid clustering and least mean squared error (LMS) algorithm. The neuro- fuzzy system under consideration is implemented as an two- layer FHRCNN (fuzzy hyperrectangular composite neural network). The SDDL (supervised decision-directed learning) algorithm is used to find a set of hyperrectangles defined by the parameters of hidden nodes while the LMS algorithm estimates the connection weights from hidden nodes to output nodes. Furthermore, based on the hybrid learning rule, the fuzzy neural networks can evolve automatically to acquire a set of fuzzy if-then rules for approximating the input/output functions of considered systems. A highly nonlinear system is used to test the proposed neural-fuzzy systems. The simulation results demonstrate its feasibility and robustness.
    關聯: Proceedings of 1994 International Symposium on Artificial Neural Networks,頁495-500
    顯示於類別:[電機工程學系暨研究所] 會議論文

    文件中的檔案:

    沒有與此文件相關的檔案.

    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