Fast Interpolation Using Kohonen Self-Organizing Neural Networks

Authors:
Olivier Sarzeaud;Yann Stéphan
Affiliations:
-;-
Venue:
TCS '00 Proceedings of the International Conference IFIP on Theoretical Computer Science, Exploring New Frontiers of Theoretical Informatics
Year:
2000

Citing 3
Cited 1

On the stationary state of Kohonen's self-organizing sensory mapping

Biological Cybernetics
Convergence properties of Kohonen's topology conserving maps: fluctuations, stability, and dimension selection

Biological Cybernetics
Self-Organizing Maps

Self-Organizing Maps

Modeling airborne benzene in space and time with self-organizing maps and Bayesian techniques

Environmental Modelling & Software

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Abstract

This paper proposes a new interpolation method based on Kohonen self-organizing networks. This method performs very well, combining an accuracy comparable with usual optimal methods (kriging) with a shorter computing time, and is especially efficient when a great amount of data is available. Under some hypothesis similar to those used for kriging, unbiasness and optimality of neural interpolation can be demonstrated. A real world problem is finally considered: building a map of surface-temperature climatology in the Mediterranean Sea. This example emphasizes the abilities of the method.