Please use this identifier to cite or link to this item:
|Title:||Data fusion in radial basis function networks for spatial regression|
Radial basis function network
|Citation:||Hu, T., Sung, S.Y. (2005). Data fusion in radial basis function networks for spatial regression. Neural Processing Letters 21 (2) : 81-93. ScholarBank@NUS Repository. https://doi.org/10.1007/s11063-004-7776-5|
|Abstract:||Conventional radial basis function (RBF) networks for spatial regression assume independent and identical distribution and ignore spatial information. In contrast to input fusion, we push spatial information further into RBF networks by fusing output from hidden and output layers. Three case studies demonstrate the advantage of hidden fusion over others and indicate the optimal value is around 1 for the coefficient used in hidden fusion, which links the output from the hidden layer for each site with their neighbors. © 2005 Springer.|
|Source Title:||Neural Processing Letters|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 17, 2018
WEB OF SCIENCETM
checked on Jun 27, 2018
checked on Jun 30, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.