Type-2 fuzzy sets: geometric defuzzification and type-reduction
This paper presents the geometric defuzzifier for general type-2 fuzzy sets. This novel method has the potential to transform the fuzzy control paradigm. General type-2 fuzzy logic is better able to model noise and uncertainty but suffers from the massive computational cost of defuzzification. This paper uses geometry to eliminate this problem, paving the way for general type-2 fuzzy control. This paper was shortlisted for the best paper award at this prestigious international conference.
Citation : Coupland, S. (2007) Type-2 fuzzy sets: geometric defuzzification and type-reduction. Proceedings of IEEE Symposium on Foundations of Computational Intelligence, pp. 622-629.
ISBN : 1424407036
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)