.png){kind=icon}
.png){kind=icon}
Return
Constrained Interval Type-2 Fuzzy Sets
.png){kind=icon}
OA
AI
DOI：10.1109/TFUZZ.2020.2970911.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In many contexts, type-2 fuzzy sets (T2 FS) are obtained from a type-1 fuzzy set to which we wish to add uncertainty. However, in the current type-2 representation, there is no restriction on the shape of the footprint of uncertainty and the embedded sets (ESs) that can be considered acceptable. This leads, usually, to the loss of the semantic relationship between the T2 FS and the concept it models. As a consequence, the interpretability of some of the ESs and the explainability of the uncertainty measures obtained from them can decrease. To overcome these issues, constrained type-2 (CT2) fuzzy sets have been proposed. However, no formal definitions for some of their key components [e.g., acceptable ESs (AESs)] and constrained operations have been given. In this article, we provide some theoretical underpinning for the definition of CT2 sets, their inferencing and defuzzification method. To conclude, the constrained inference framework is presented, applied to two real-world cases and briefly compared to the standard interval type-2 inference and defuzzification method.
Keywords:
Fuzzy sets
Shape
Uncertainty
Fuzzy systems
Standards
Licenses
Fuzzy logic
Constrained type-2 (CT2) fuzzy sets (CT2 FS)
embedded sets (ESs)
type-2 fuzzy logic
explainable artificial intelligence (XAI)
Journal
IF:
11.9
Papers:
4.9K
Citations:
2.9W