Learning fuzzy-valued fuzzy measures for the fuzzy-valued Sugeno fuzzy integral

Authors:
Derek T. Anderson;James M. Keller;Timothy C. Havens
Affiliations:
Electrical and Computer Engineering Department, University of Missouri, Columbia, MO;Electrical and Computer Engineering Department, University of Missouri, Columbia, MO;Electrical and Computer Engineering Department, University of Missouri, Columbia, MO
Venue:
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Year:
2010

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Abstract

Fuzzy integrals are very useful for fusing confidence or opinions from a variety of sources. These integrals are non-linear combinations of the support functions with the (possibly subjective) worth of subsets of the sources, realized by a fuzzy measure. There have been many applications and extensions of fuzzy integrals and this paper deals with a Sugeno integral where both the integrand and the measure take on fuzzy number values. A crucial aspect of using fuzzy integrals for fusion is determining or learning the measures. Here, we propose a genetic algorithm with novel cross-over and mutation operators to learn fuzzy-valued fuzzy measures for a fuzzy-valued Sugeno integral.