English  |  正體中文  |  简体中文  |  Items with full text/Total items : 52047/87178 (60%)
Visitors : 8692659      Online Users : 152
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/987654321/68571

    Title: Fuzzy regression with radial basis function network
    Authors: 鄭啟斌;Lee, E. S.
    Contributors: 淡江大學資訊管理學系
    Keywords: Regression analysis;Nonparametric fuzzy regression;Fuzzy radial basis network
    Date: 2001-09-28
    Issue Date: 2011-10-23 13:17:07 (UTC+8)
    Abstract: Radial basis function network is used in fuzzy regression analysis without predefined functional relationship between the input and the output. The proposed approach is a fuzzification of the connection weights between the hidden and the output layers. This fuzzy network is trained by a hybrid learning algorithm, where self-organized learning is used for training the parameters of the hidden units and supervised learning is used for updating the weights between the hidden and the output layers. The c-mean clustering method and the k-nearest-neighbor heuristics are used for the self-organized learning. The supervised learning is carried out by solving a linear possibilistic programming problem. Techniques for the generalization of the network are also proposed. Numerical examples are used to illustrate and to test the performances of the approach.
    Relation: Fuzzy Sets and Systems 119, pp.291-301
    DOI: 10.1016/S0165-0114(99)00098-6
    Appears in Collections:[資訊管理學系暨研究所] 期刊論文

    Files in This Item:

    There are no files associated with this item.

    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