This paper presents a universal methodology for generating an interval type-2 fuzzy set membership function from a collection of type-1 fuzzy sets. The key idea of the proposed methodology is to designate a specific type-1 fuzzy set as the representative of all input type-1 fuzzy sets. To this end, we use a novel measure of similarity between type-1 fuzzy sets, which relies on both kernel functions and fuzzy information processing methods. Based on the selected representative type-1 fuzzy set, and with respect to the principle of justifiable granularity, an interval type-2 fuzzy set is then formed. The results of the conducted experiments demonstrate the effectiveness of the proposed methodology for generating sound interval type-2 fuzzy sets.
This paper presents a universal methodology for generating an interval type-2 fuzzy set membership function from a collection of type-1 fuzzy sets. The key idea of the proposed methodology is to designate a specific type-1 fuzzy set as the representative of all input type-1 fuzzy sets. To this end, we use a novel measure of similarity between type-1 fuzzy sets, which relies on both kernel functions and fuzzy information processing methods. Based on the selected representative type-1 fuzzy set, and with respect to the principle of justifiable granularity, an interval type-2 fuzzy set is then formed. The results of the conducted experiments demonstrate the effectiveness of the proposed methodology for generating sound interval type-2 fuzzy sets.
Interval type-2 fuzzy set reconstruction based on fuzzy information-theoretic kernels / Hooman, Tahayori; Livi, Lorenzo; Alireza, Sadeghian; Rizzi, Antonello. - In: IEEE TRANSACTIONS ON FUZZY SYSTEMS. - ISSN 1063-6706. - STAMPA. - 23:4(2015), pp. 1014-1029. [10.1109/tfuzz.2014.2336673]
Interval type-2 fuzzy set reconstruction based on fuzzy information-theoretic kernels
LIVI, LORENZO;RIZZI, Antonello
2015
Abstract
This paper presents a universal methodology for generating an interval type-2 fuzzy set membership function from a collection of type-1 fuzzy sets. The key idea of the proposed methodology is to designate a specific type-1 fuzzy set as the representative of all input type-1 fuzzy sets. To this end, we use a novel measure of similarity between type-1 fuzzy sets, which relies on both kernel functions and fuzzy information processing methods. Based on the selected representative type-1 fuzzy set, and with respect to the principle of justifiable granularity, an interval type-2 fuzzy set is then formed. The results of the conducted experiments demonstrate the effectiveness of the proposed methodology for generating sound interval type-2 fuzzy sets.File | Dimensione | Formato | |
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Tahayori_Interval-type-2_2015.pdf
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Note: Interval Type-2 Fuzzy Set Reconstruction Based on Fuzzy Information-Theoretic Kernels
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