The defuzzification of a type-2 fuzzy set is a two-stage process consisting of firstly type-reduction, and a secondly defuzzification of the resultant type-1 set. All accurate type reduction methods used to build fuzzy classifiers are based on the recursive Karnik-Mendel algorithm, which is troublesome to obtain a feedforward type-2 fuzzy network structure. Moreover, the KM algorithm and its modifications complicate the learning process due to the non-differentiability of the maximum and minimum functions. Therefore, this paper proposes to use the smooth maximum function to develop a new structure of the fuzzy type-2 classifier.
Type-2 Fuzzy Classifier with Smooth Type-Reduction / Nieszporek, K; De Magistris, G; Napoli, C; Starczewski, Jt. - 13588:(2023), pp. 193-202. (Intervento presentato al convegno Artificial Intelligence and Soft Computing tenutosi a Zakopane; Poland) [10.1007/978-3-031-23492-7_17].
Type-2 Fuzzy Classifier with Smooth Type-Reduction
De Magistris, GSecondo
Formal Analysis
;Napoli, CPenultimo
Supervision
;
2023
Abstract
The defuzzification of a type-2 fuzzy set is a two-stage process consisting of firstly type-reduction, and a secondly defuzzification of the resultant type-1 set. All accurate type reduction methods used to build fuzzy classifiers are based on the recursive Karnik-Mendel algorithm, which is troublesome to obtain a feedforward type-2 fuzzy network structure. Moreover, the KM algorithm and its modifications complicate the learning process due to the non-differentiability of the maximum and minimum functions. Therefore, this paper proposes to use the smooth maximum function to develop a new structure of the fuzzy type-2 classifier.| File | Dimensione | Formato | |
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Nieszporek_Preprint_Type-2_2022.pdf
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