A Fuzzy Rule-Based Classification System Using Interval Type-2 Fuzzy Sets

2011 ◽  
pp. 72-80 ◽  
Author(s):  
Min Tang ◽  
Xia Chen ◽  
Weidong Hu ◽  
Wenxian Yu
Keyword(s):  
Fuzzy Sets ◽  
Classification System ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Interval Type

Fuzzy rule interpolation based on the ratio of fuzziness of interval type-2 fuzzy sets

2011 ◽  
Vol 38 (10) ◽  
pp. 12202-12213 ◽  
Author(s):  
Shyi-Ming Chen ◽  
Yu-Chuan Chang
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Rule ◽  
Interval Type

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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