English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51296/86412 (59%)
Visitors : 8174317      Online Users : 92
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/987654321/91599


    Title: Spatial interpolation using MLP–RBFN hybrid networks
    Authors: Yeh, I-Cheng;Kuan-Chieh Huang;Yau-Hwang Kuo
    Contributors: 淡江大學土木工程學系
    Keywords: spatial interpolation;multi-layered perception;radial basis function network;hybrid network
    Date: 2013-10
    Issue Date: 2013-07-11 16:22:20 (UTC+8)
    Publisher: Abingdon: Taylor & Francis
    Abstract: It is easy for a multi-layered perception (MLP) to fit a stratified spatial interpolation pattern whose form is close to open surface; while it is easy for a radial basis function network (RBFN) to fit a pocket (radial) spatial interpolation pattern whose form is close to closed surface. However, in the real world, the spatial interpolation pattern may consist of stratified and pocket patterns. Neither MLP nor RBFN can fit the pattern easily. To combine their advantages to fit the complex hybrid spatial interpolation patterns, in this article we propose a novel neural network, MLP–RBFN hybrid network (MRHN), whose hidden layer contains sigmoid and Gaussian units at the same time. Although there are two kinds of processing units in MRHN, in this study we used the principle of minimizing the error sum of squares to derive the supervised learning rules for all the network parameters. This research took rainfall distribution in Taiwan as a case study. The results show that (1) the prediction error of the testing dataset outside the training dataset demonstrated that MRHN was the most accurate among the three networks, RBFN was the next best, and MLP was the worst; (2) the MLP model seriously underestimated the values of high observed rainfall; (3) over-learning may be a serious shortcoming of using RBFN in spatial interpolation applications; (4) MRHN may have better generalization learning capacity than RBFN in spatial interpolation applications.
    Relation: International Journal of Geographical Information Science 27(10), pp.1884-1901
    DOI: 10.1080/13658816.2013.769050
    Appears in Collections:[土木工程學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML112View/Open
    Spatial interpolation using MLP–RBFN hybrid networks.pdfSpatial interpolation3961KbAdobe PDF354View/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