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Data Fusion in Radial Basis Function Networks for Spatial Regression

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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.

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Abbreviations

RBF:

Radial Basis Function

MSE:

Mean Squared Error

iid:

independent and identical distribution

IF:

Input Fusion

HF:

Hidden Fusion

OF:

Output Fusion

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Authors and Affiliations

  1. Department of ComputerScience, National University of Singapore, Singapore, 117543

    Tianming Hu & Sam Yuan sung

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  1. Tianming Hu
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  2. Sam Yuan sung
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Correspondence to Tianming Hu.

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Hu, T., sung, S.Y. Data Fusion in Radial Basis Function Networks for Spatial Regression. Neural Process Lett 21, 81–93 (2005). https://doi.org/10.1007/s11063-004-7776-5

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  • DOI: https://doi.org/10.1007/s11063-004-7776-5

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