Fuzzy logic has become increasingly prominent in addressing uncertainties inherent in decision-making processes across various domains. A pivotal component of fuzzy systems is the rule base, which establishes the relationships between inputs and outputs. While traditionally constructed based on expert knowledge or iterative learning from numerical data, there is a growing need to design rule bases solely from numerical data in scenarios lacking expert input. This paper explores methodologies for constructing rule bases from numerical data, considering challenges such as selecting appropriate membership functions and determining rule structures.
| Mualliflar | Ergashev, Otabek |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2024-12-28 |
| Son | 4 |
| Betlar | 422-428 |
| Til | Rus |
Fuzzy logic, Rule base design, Numerical data, Membership functions, Fuzzy systems, Decision-making, Uncertainty, Expert systems, Control systems, Data-driven modeling.
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