On Computing the Footprint of Uncertainty of an Interval Type-2 Fuzzy Set as Uncertainty Measure

2016 ◽  
pp. 247-257 ◽  
Author(s):  
Juan Carlos Figueroa-García ◽  
Germán Jairo Hernández-Pérez ◽  
Yurilev Chalco-Cano
Keyword(s):  
Fuzzy Set ◽  
Uncertainty Measure ◽  
Footprint Of Uncertainty ◽  
Interval Type

scholarly journals Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

2021 ◽  
Vol 11 (8) ◽  
pp. 3484
Author(s):  
Martin Tabakov ◽  
Adrian Chlopowiec ◽  
Adam Chlopowiec ◽  
Adam Dlubak
Keyword(s):  
Fuzzy Inference ◽  
Real Data ◽  
Fuzzy Rules ◽  
Fuzzy Information ◽  
Benchmark Data ◽  
System A ◽  
Footprint Of Uncertainty ◽  
Interval Type ◽  
Derived Rules

In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.

Representing a probabilistic linguistic term set with an interval type-2 fuzzy set and the application in green supplier selection

2021 ◽  
pp. 1-18
Author(s):  
Le Jiang ◽  
Hongbin Liu
Keyword(s):  
Decision Making ◽  
Membership Function ◽  
Fuzzy Set ◽  
Supplier Selection ◽  
Symbolic Model ◽  
Green Supplier Selection ◽  
Linguistic Term ◽  
Interval Type ◽  
Decision Making Model

The use of probabilistic linguistic term sets (PLTSs) means the process of computing with words. The existing methods computing with PLTSs mainly use symbolic model. To provide a semantic model for computing with PLTSs, we propose to represent a PLTS by using an interval type-2 fuzzy set (IT2FS). The key step is to compute the footprint of uncertainty of the IT2FS. To this aim, the upper membership function is computed by aggregating the membership functions of the linguistic terms contained in the PLTS, and the lower membership function is obtained by moving the upper membership function downward with the step being total entropy of the PLTS. The comparison rules, some operations, and an aggregation operator for PLTSs are introduced. Based on the proposed method of computing with PLTSs, a multi-criteria group decision making model is introduced. The proposed decision making model is then applied in green supplier selection problem to show its feasibility.

An Efficient Non-Iterative Method for Computing the Centroid of an Interval Type-2 Fuzzy Set

2021 ◽  
Keyword(s):  
Iterative Method ◽  
Fuzzy Set ◽  
Interval Type

Abstract The authors have requested that this preprint be withdrawn due to author disagreement.

An Extended TOPSIS Method Based on Gaussian Interval Type-2 Fuzzy Set

2019 ◽  
Vol 21 (6) ◽  
pp. 1831-1843
Author(s):  
Huidong Wang ◽  
Jinli Yao ◽  
Jun Yan ◽  
Mingguang Dong
Keyword(s):  
Fuzzy Set ◽  
Topsis Method ◽  
Interval Type
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