Fuzzy Rules Interpolation for Sparse Fuzzy Rule-Based Systems Based on Interval Type-2 Gaussian Fuzzy Sets and Genetic Algorithms

2013 ◽  
Vol 21 (3) ◽  
pp. 412-425 ◽  
Author(s):  
Shyi-Ming Chen ◽  
Yu-Chuan Chang ◽  
Jeng-Shyang Pan
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Sets ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Rule Based ◽  
Interval Type ◽  
Rule Based Systems

Type-2 interval fuzzy rule-based systems in spatial analysis

2014 ◽  
Vol 279 ◽  
pp. 199-212 ◽  
Author(s):  
Ferdinando Di Martino ◽  
Salvatore Sessa
Keyword(s):  
Spatial Analysis ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Rule Based Systems

scholarly journals Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems

2020 ◽  
Vol 10 (4) ◽  
pp. 271-285
Author(s):  
Janusz T. Starczewski ◽  
Piotr Goetzen ◽  
Christian Napoli
Keyword(s):  
Uncertain Data ◽  
Fuzzy Rule ◽  
Experimental System ◽  
Rule Based ◽  
Fuzzy Methods ◽  
Rule Sets ◽  
Fuzzy Partitions ◽  
Interval Type ◽  
Triangular Membership Function

AbstractIn real-world approximation problems, precise input data are economically expensive. Therefore, fuzzy methods devoted to uncertain data are in the focus of current research. Consequently, a method based on fuzzy-rough sets for fuzzification of inputs in a rule-based fuzzy system is discussed in this paper. A triangular membership function is applied to describe the nature of imprecision in data. Firstly, triangular fuzzy partitions are introduced to approximate common antecedent fuzzy rule sets. As a consequence of the proposed method, we obtain a structure of a general (non-interval) type-2 fuzzy logic system in which secondary membership functions are cropped triangular. Then, the possibility of applying so-called regular triangular norms is discussed. Finally, an experimental system constructed on precise data, which is then transformed and verified for uncertain data, is provided to demonstrate its basic properties.

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