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Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets.
- Source :
-
Algorithms . Sep2017, Vol. 10 Issue 3, p91. 20p. - Publication Year :
- 2017
-
Abstract
- In support of reasoning with sparse rule bases, fuzzy rule interpolation (FRI) offers a helpful inference mechanism for deriving an approximate conclusion when a given observation has no overlap with any rule in the existing rule base. One of the recent and popular FRI approaches is the scale and move transformation-based rule interpolation, known as T-FRI in the literature. It supports both interpolation and extrapolation with multiple multi-antecedent rules. However, the difficult problem of defining the precise-valued membership functions required in the representation of fuzzy rules, or of the observations, restricts its applications. Fortunately, this problem can be alleviated through the use of type-2 fuzzy sets, owing to the fact that the membership functions of such fuzzy sets are themselves fuzzy, providing a more flexible means of modelling. This paper therefore, extends the existing T-FRI approach using interval type-2 fuzzy sets, which covers the original T-FRI as its specific instance. The effectiveness of this extension is demonstrated by experimental investigations and, also, by a practical application in comparison to the state-of-the-art alternative approach developed using rough-fuzzy sets. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19994893
- Volume :
- 10
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Algorithms
- Publication Type :
- Academic Journal
- Accession number :
- 125323101
- Full Text :
- https://doi.org/10.3390/a10030091