Construction of Fuzzy Membership Functions Using Interactive Self-Organizing Maps
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.
T. E. Sandidge and C. H. Dagli, "Construction of Fuzzy Membership Functions Using Interactive Self-Organizing Maps," Proceedings of SPIE, SPIE -- The International Society for Optical Engineering, Apr 1998.
The definitive version is available at http://dx.doi.org/10.1117/12.304817
Engineering Management and Systems Engineering
Keywords and Phrases
Kohonen Maps; Limitations; Network; Self-organizing maps
Article - Conference proceedings
© 1998 SPIE -- The International Society for Optical Engineering, All rights reserved.