Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems
| Autor: | Christian Napoli, Piotr Goetzen, Janusz T. Starczewski |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
regular type-2 t-norms Computer science business.industry fuzzy-rough fuzzification Fuzzy set general type-2 fuzzy logic systems 02 engineering and technology cropped triangular secondary membership functions 020901 industrial engineering & automation Artificial Intelligence Hardware and Architecture Modeling and Simulation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Fuzzy rule based systems Computer Vision and Pattern Recognition Artificial intelligence Fuzzy rough sets business Information Systems |
| Zdroj: | Journal of Artificial Intelligence and Soft Computing Research. 10:271-285 |
| ISSN: | 2083-2567 |
| DOI: | 10.2478/jaiscr-2020-0018 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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