Interval type-2 defuzzification using uncertainty weights

Runkler, Thomas A., Coupland, Simon, John, Robert and Chen, Chao (2017) Interval type-2 defuzzification using uncertainty weights. Studies in Computational Intelligence, 739 . ISSN 1860-949X

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One of the most popular interval type-2 defuzzification methods is the Karnik-Mendel (KM) algorithm. Nie and Tan (NT) have proposed an approximation of the KM method that converts the interval type-2 membership functions to a single type-1 membership function by averaging the upper and lower memberships, and then applies a type-1 centroid defuzzification. In this paper we propose a modification of the NT algorithm which takes into account the uncertainty of the (interval type-2) memberships. We call this method the uncertainty weight (UW) method. Extensive numerical experiments motivated by typical fuzzy controller scenarios compare the KM, NT, and UW methods. The experiments show that (i) in many cases NT can be considered a good approximation of KM with much lower computational complexity, but not for highly unbalanced uncertainties, and (ii) UW yields more reasonable results than KM and NT if more certain decision alternatives should obtain a larger weight than more uncertain alternatives.

Item Type: Article
Additional Information: The final publication is available at via Published as: Runkler T.A., Coupland S., John R., Chen C. (2018) Interval Type–2 Defuzzification Using Uncertainty Weights. In: Mostaghim S., Nürnberger A., Borgelt C. (eds) Frontiers in Computational Intelligence. Studies in Computational Intelligence, vol 739. Springer, Cham
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
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Depositing User: Eprints, Support
Date Deposited: 17 Oct 2017 11:08
Last Modified: 27 Sep 2018 04:30

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