In real-world approximation problems, precise input data are economically expensive. Therefore, fuzzy methods devoted to uncertain data are in the focus of current research. Consequently, a method based on fuzzy-rough sets for fuzzification of inputs in a rule-based fuzzy system is discussed in this paper. A triangular membership function is applied to describe the nature of imprecision in data. Firstly, triangular fuzzy partitions are introduced to approximate common antecedent fuzzy rule sets. As a consequence of the proposed method, we obtain a structure of a general (non-interval) type-2 fuzzy logic system in which secondary membership functions are cropped triangular. Then, the possibility of applying so-called regular triangular norms is discussed. Finally, an experimental system constructed on precise data, which is then transformed and verified for uncertain data, is provided to demonstrate its basic properties.
Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems / Starczewski, Janusz T.; Goetzen, Piotr; Napoli, Christian. - In: JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH. - ISSN 2083-2567. - 10:4(2020), pp. 271-285.
Titolo: | Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems |
Autori: | NAPOLI, CHRISTIAN (Ultimo) [Supervision] |
Data di pubblicazione: | 2020 |
Rivista: | |
Citazione: | Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems / Starczewski, Janusz T.; Goetzen, Piotr; Napoli, Christian. - In: JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH. - ISSN 2083-2567. - 10:4(2020), pp. 271-285. |
Handle: | http://hdl.handle.net/11573/1421642 |
Appartiene alla tipologia: | 01a Articolo in rivista |
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