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Proceedings Paper

Construction of fuzzy membership functions using interactive self-organizing maps
Author(s): Thomas E. Sandidge; Cihan H. Dagli
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Paper Abstract

This paper presents a Kohonen-like mapping that eliminates or reduces four limitations of the Kohonen maps. The described network is invariant to scale, very resistant to 'automatic selection of feature dimensions,' results in strictly ordered clusters of ascending/descending magnitude, and may allow a greater amount of information to be gleaned from high dimensional data sets. The network treats each input component separately but each map is influenced via inter-map connections. Unfortunately, processing time increases combinatorially as the number of input components and number of neurons per component increases. As a demonstration, membership functions are constructed for a four variable data set with minimal parameter setting, the most crucial being the number of classes per input component.

Paper Details

Date Published: 25 March 1998
PDF: 5 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304817
Show Author Affiliations
Thomas E. Sandidge, Univ. of Missouri/Rolla (United States)
Cihan H. Dagli, Univ. of Missouri/Rolla (United States)

Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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