Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39626
Title: Predicting spatial data with RBF networks.
Authors: Hu, T.
Sung, S.Y. 
Issue Date: 2004
Source: Hu, T.,Sung, S.Y. (2004). Predicting spatial data with RBF networks.. International journal of neural systems 14 (2) : 117-123. ScholarBank@NUS Repository.
Abstract: Spatial prediction needs to account for spatial information, which makes conventional radial basis function (RBF) networks inappropriate, for they assume independent and identical distribution. In this paper, we fuse spatial information at different layers of RBF. Experiments show fusion at hidden layer gives the best result and suggest that the optimal value is around one for the coefficient, which is used in the linear combination at the output layer.
Source Title: International journal of neural systems
URI: http://scholarbank.nus.edu.sg/handle/10635/39626
ISSN: 01290657
Appears in Collections:Staff Publications

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