Now showing 1 - 1 of 1
No Thumbnail Available

Application of Fuzzy Rule Base Design Method

2016, Grabusts, Pēteris

In many classification tasks the final goal is usually to determine classes of objects. The final goal of fuzzy clustering is also the distribution of elements with highest membership functions into classes. The key issue is the possibility of extracting fuzzy rules that describe clustering results. The paper develops a method of fuzzy rule base designing for the numerical data, which enables extracting fuzzy rules in the form IFTHEN. To obtain the membership functions, the fuzzy c-means clustering algorithm is employed. The described methodology of fuzzy rule base designing allows one to classify the data. The practical part contains implementation examples.