Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets
AbstractIn 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. View Full-Text
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Chen, C.; Shen, Q. Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets. Algorithms 2017, 10, 91.
Chen C, Shen Q. Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets. Algorithms. 2017; 10(3):91.Chicago/Turabian Style
Chen, Chengyuan; Shen, Qiang. 2017. "Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets." Algorithms 10, no. 3: 91.