English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 49433/84396 (59%)
造訪人次 : 7465018      線上人數 : 79
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/987654321/91599

    題名: Spatial interpolation using MLP–RBFN hybrid networks
    作者: Yeh, I-Cheng;Kuan-Chieh Huang;Yau-Hwang Kuo
    貢獻者: 淡江大學土木工程學系
    關鍵詞: spatial interpolation;multi-layered perception;radial basis function network;hybrid network
    日期: 2013-10
    上傳時間: 2013-07-11 16:22:20 (UTC+8)
    出版者: Abingdon: Taylor & Francis
    摘要: 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.
    關聯: International Journal of Geographical Information Science 27(10), pp.1884-1901
    DOI: 10.1080/13658816.2013.769050
    顯示於類別:[土木工程學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數
    Spatial interpolation using MLP–RBFN hybrid networks.pdfSpatial interpolation3961KbAdobe PDF347檢視/開啟



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