scholarly journals Editorial Message: Special Issue on Advances in Fuzzy Systems and Type-2 Fuzzy Sets

2020 ◽  
Vol 22 (1) ◽  
pp. 337-337
Author(s):  
Jin-Tsong Jeng ◽  
Byung-Jae Choi
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Systems ◽  
Special Issue

Guest Editorial for the special issue on type-2 fuzzy sets and systems

2013 ◽  
Vol 21 (3) ◽  
pp. 397-398 ◽  
Author(s):  
Jerry M. Mendel ◽  
Hani Hagras ◽  
Robert I. John
Keyword(s):  
Fuzzy Sets ◽  
Guest Editorial ◽  
Special Issue

Fuzzy Sets of Type-2

1999 ◽  
Vol 3 (6) ◽  
pp. 499-508 ◽  
Author(s):  
Robert John ◽  
Keyword(s):  
Knowledge Representation ◽  
Fuzzy Sets ◽  
Fuzzy Systems ◽  
Medical Application ◽  
The Other ◽  
Detailed Review

This paper provides a detailed review of the important and growing role that fuzzy sets of type-2 play in knowledge representation and inferencing with fuzzy systems. As well as an up-to-date review of the work in this area, examples are provided that demonstrate how type-2 sets can help with both knowledge representation and inferencing. The paper also reports on the use of type-2 sets in a medical application and summarizes the other type-2 applications reported in the literature.

Overview of Type-2 Fuzzy Logic Systems

2012 ◽  
Vol 2 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Ahmad Taher Azar
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Systems ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Systems ◽  
Logic Systems

Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 fuzzy set (T1FS) does not capture uncertainty in its manifestations when it arises from vagueness in the shape of the membership function. Such uncertainties need to be depicted by fuzzy sets that have blur boundaries. The imprecise boundaries of a type-2 fuzzy set (T2FS) give rise to truth/membership values that are fuzzy sets in [0], [1], instead of a crisp number. Type-2 fuzzy logic systems (T2FLSs) offer opportunity to model levels of uncertainty which traditional fuzzy logic type1 struggles. This extra dimension gives more degrees of freedom for better representation of uncertainty compared to type-1 fuzzy sets. A type-1 fuzzy logic system (T1FLSs) inference produces a T1FS and the result of defuzzification of the T1FS, a crisp number, whereas a T2FLS inference produces a type-2 fuzzy set, its type-reduced fuzzy set which is a T1FS and the defuzzification of the type-1 fuzzy set. The type-reduced fuzzy set output gives decision-making flexibilities. Thus, FLSs using T2FS provide the capability of handling a higher level of uncertainty and provide a number of missing components that have held back successful deployment of fuzzy systems in decision making.

scholarly journals Special issue on “Type-2 fuzzy systems and granular computing”

2018 ◽  
Vol 4 (2) ◽  
pp. 143-143 ◽  
Author(s):  
Oscar Castillo ◽  
Pranab K. Muhuri
Keyword(s):  
Fuzzy Systems ◽  
Granular Computing ◽  
Special Issue

scholarly journals Editorial on Special Issue: “Trends and Developments on Type-2 Fuzzy Sets and Systems”

2021 ◽  
Author(s):  
Oscar Castillo ◽  
Pranab Kumar Muhuri ◽  
Hong Mo ◽  
Chengdong Li ◽  
Runmei Li ◽  
...  
Keyword(s):  
Fuzzy Sets ◽  
Special Issue
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