A new neural network-based type reduction algorithm for interval type-2 fuzzy logic systems

2013 ◽  
Author(s):  
Abbas Khosravi ◽  
Saeid Nahavandi ◽  
Rihanna Khosravi
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Reduction Algorithm ◽  
Fuzzy Logic Systems ◽  
Type Reduction ◽  
Logic Systems ◽  
Interval Type

Study on center-of-sets type-reduction of interval type-2 fuzzy logic systems with noniterative algorithms

2021 ◽  
pp. 1-8
Author(s):  
Yang Chen ◽  
Jiaxiu Yang
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Reasoning ◽  
Fuzzy Logic Systems ◽  
Simulation Experiments ◽  
Potential Value ◽  
Type Reduction ◽  
Logic Systems ◽  
Interval Type ◽  
Open Question

In recent years, interval type-2 fuzzy logic systems (IT2 FLSs) have become a hot topic for the capability of coping with uncertainties. Compared with the centroid type-reduction (TR), investigating the center-of-sets (COS) TR of IT2 FLSs is more favorable for applying IT2 FLSs. Actually, it is still an open question for comparing Karnik-Mendel (KM) types of algorithms and other types of alternative algorithms for COS TR. This paper gives the block of fuzzy reasoning, COS TR, and defuzzification of IT2 FLSs based on Nagar-Bardini (NB), Nie-Tan (NT) and Begian-Melek-Mendel (BMM) noniterative algorithms. Six simulation experiments are used to show the performances of three types of noniterative algorithms. The proposed noniterative algorithms can obtain much higher computational efficiencies compared with the KM algorithms, which give the potential value for designing T2 FLSs.

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