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.
|Titolo:||Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems|
NAPOLI, CHRISTIAN (Ultimo) [Supervision]
|Data di pubblicazione:||2020|
|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.|
|Appartiene alla tipologia:||01a Articolo in rivista|