English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88613/118155 (75%)
Visitors : 23472370      Online Users : 170
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU 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
    政大機構典藏 > 理學院 > 資訊科學系 > 期刊論文 >  Item 140.119/75227
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/75227


    Title: Spatial interpolation using MLP-RBFN hybrid networks
    Authors: Kuo, Yau-Hwang
    郭耀煌
    Huang, K.-C.
    Yeh, I.-C.
    Contributors: 資科系
    Keywords: artificial neural network;interpolation;rainfall;spatial analysis;spatial distribution;Taiwan
    Date: 2013-10
    Issue Date: 2015-05-21 16:15:55 (UTC+8)
    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. © 2013 Taylor & Francis.
    Relation: International Journal of Geographical Information Science, 27(10), 1884-1901
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1080/13658816.2013.769050
    DOI: 10.1080/13658816.2013.769050
    Appears in Collections:[資訊科學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    1884-1901.pdf1076KbAdobe PDF439View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback