Uncertainty measures for general type-2 fuzzy sets

Authors:
Daoyuan Zhai;Jerry M. Mendel
Affiliations:
Signal and Image Processing Institute, Ming Hsieh Department of Electrical Engineering, University of Southern, California Los Angeles, CA;Signal and Image Processing Institute, Ming Hsieh Department of Electrical Engineering, University of Southern, California Los Angeles, CA
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

Citing 12
Cited 0

Fuzzy sets, uncertainty, and information

Fuzzy sets, uncertainty, and information
Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets

Fuzzy Sets and Systems
Entropy for intuitionistic fuzzy sets

Fuzzy Sets and Systems
Cardinality concepts for type-two fuzzy sets

Fuzzy Sets and Systems
Centroid of a type-2 fuzzy set

Information Sciences: an International Journal
A fuzzy sets based linguistic approach: Theory and applications

WSC '80 Proceedings of the 12th conference on Winter simulation
Uncertainty measures for interval type-2 fuzzy sets

Information Sciences: an International Journal
An efficient centroid type-reduction strategy for general type-2 fuzzy logic system

Information Sciences: an International Journal
Enhanced Karnik-Mendel algorithms

IEEE Transactions on Fuzzy Systems
Type-2 fuzzy sets made simple

IEEE Transactions on Fuzzy Systems
Super-Exponential Convergence of the Karnik–Mendel Algorithms for Computing the Centroid of an Interval Type-2 Fuzzy Set

IEEE Transactions on Fuzzy Systems
Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Five uncertainty measures have previously been defined for interval type-2 fuzzy sets (IT2 FSs), namely centroid, cardinality, fuzziness, variance and skewness. Based on a recently developed α-plane representation technique, this paper generalizes these definitions to general T2 FSs and, more importantly, derives a unified strategy for computing all different uncertainty measures with low complexity. The uncertainty measures of T2 FSs with different shaped Footprints of Uncertainty (FOU) and different triangular secondary membership functions are computed and are given as examples.