English  |  正體中文  |  简体中文  |  Items with full text/Total items : 60868/93650 (65%)
Visitors : 1152377      Online Users : 18
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: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/96021

    Title: Generating Diagnostic Rules Directly from Experimental Data
    Authors: Su, Mu-Chun
    Contributors: 淡江大學電機工程學系
    Keywords: 診斷;實驗數據;類神經網路;模糊系統;Diagnosis;Experimental Data;Neural Network;Fuzzy System
    Date: 1996-05
    Issue Date: 2014-02-13 11:35:44 (UTC+8)
    Abstract: Traditionally, a major task in building a medical diagnosis expert system is the process of acquiring the required knowledge in the form of production rules(IF...THEN...). Alternative knowledge acquisition approach to articulating knowledge required for diagnostic tasks are presented in this paper. Each approach has its own advantages and disadvantages. The ultimate goal of these approaches is to free human experts from tedious diagnosis loads. The effectiveness of these approaches is demonstrated by an example of a hypothesis regarding the pathophysiology of diabetes.
    Relation: 第二屆國際醫學工程週論文集=Proceedings of the 2nd Medical Engineering Week of the World,頁438-443
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Proceeding

    Files in This Item:

    File SizeFormat
    Generating Diagnostic Rules Directly from Experimental Data_英文摘要.docx15KbMicrosoft Word78View/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