English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51962/87093 (60%)
Visitors : 8511002      Online Users : 52
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/95089


    Title: Evaluation on Neural Network and Fuzzy Method---In Terms of Learning
    Authors: Lee, Hung-Chang;Wang, Tao Jung
    Contributors: 淡江大學資訊管理學系
    Keywords: 模糊學習;逆傳遞類神經網路;模糊規則產生器;Fuzzy Learning;Back Propagation Neural Network;Fuzzy Rule Generator
    Date: 1996-12
    Issue Date: 2014-02-11 15:31:13 (UTC+8)
    Abstract: Like a dawn light scattering into the cloud sky of A.I., Neural Network and Fuzzy Logic become state-of-the- art technologies in exploring the intellectual. To make a judgment between both technologies, we propose an evaluation on them in the view point of learning to classification. Since there are varieties models proposed within both technologies, we focus on most significant model, i.e., Back Propagation Network (BPN) [1] and Wang's fuzzy rule generator [2]. First in the evaluation, we introduce a Gravity Effect Field to illustrate these two models' influence under the existence of one instance. After that, we virtually construct two classifications problems and discuss the behaviors of both methods through the Gravity Effect Field. Finally, we propose another two real examples to demonstrate the results. We conclude that Wang's more suitable for the piecewise region classification and need representative or complete training samples than BPN. BPN is more training data tolerant and less network parameter sensible than that of Wang's fuzzy rule generator. However, basic instinct problems still exist, BPN behaviors more black box than fuzzy rule generator.
    Relation: Proceedings of the 1996 Asian Fuzzy Systems Symposium--Soft Computing in Intelligent Systems and Information Processing,頁79-84
    Appears in Collections:[資訊管理學系暨研究所] 會議論文

    Files in This Item:

    File SizeFormat
    Evaluation on Neural Network and Fuzzy Method---In Terms of Learning_英文摘要.docx14KbMicrosoft Word125View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


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