Spatial interpolation using MLP–RBFN hybrid networks
學年 102
學期 1
出版（發表）日期 2013-10-01
作品名稱 Spatial interpolation using MLP–RBFN hybrid networks
作品名稱（其他語言）
著者 Yeh, I-Cheng; Kuan-Chieh Huang; Yau-Hwang Kuo
單位 淡江大學土木工程學系
出版者 Abingdon: Taylor & Francis
著錄名稱、卷期、頁數 International Journal of Geographical Information Science 27(10), pp.1884-1901
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.
關鍵字 spatial interpolation; multi-layered perception; radial basis function network; hybrid network
語言 en_US
ISSN 1365-8816 1362-3087
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Huang, Kuan-Chieh
審稿制度 是
國別 GBR
公開徵稿
出版型式 紙本 電子版
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