Automatic generation of fuzzy inference systems using heuristic possibilistic clustering

• Autorzy: Viattchenin D. A.
• Języki publikacji: EN

Abstrakty

EN

The interpretability and flexibility of fuzzy classification rules make them a popular basis for fuzzy controllers. Fuzzy control methods constitute a part of the areas of automation and robotics. The paper deals with the method of extracting fuzzy classification rules based on a heuristic method of possibilistic clustering. The description of basic concepts of the heuristic method of possibilistic clustering based on the allotment concept is provided. A general plan of the D-AFC(c)-algorithm is also given. A method of constructing and tuning of fuzzy rules based on clustering results is proposed. An illustrative example of the method's application to the Anderson's Iris data is carried out. An analysis of the experimental results is given and preliminary conclusions are formulated.

Słowa kluczowe

EN

possibilistic clustering; fuzzy cluster; typical point; tolerance threshold; fuzzy rule

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2010
• Tom: Vol. 4, No. 3
• Strony: 36--44
• Opis fizyczny: Bibliogr. 21 poz., rys.

Twórcy

autor

Viattchenin D. A.

• Laboratory of Images Recognition and Processing, United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Surganov St. 6, 220012 Minsk, Belarus,

Bibliografia

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