A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Setting a rule base for a fuzzy classifier using the grasshopper optimization algorithm and the clustering algorithm
Формирование базы правил нечёткого классификатора с помощью метаэвристического алгоритма «саранчи»
2022
Proceedings of Tomsk State University of Control Systems and Radioelectronics
Формирование базы правил нечёткого классификатора с помощью метаэвристического алгоритма «саранчи»
Preserved Fulltext
The article presents a description of a hybrid algorithm for generating fuzzy rules for a fuzzy classifier using grasshopper optimization algorithm and the K-means data clustering algorithm. The performance of clustering was evaluated by three fitness functions: total variance, Davis–Bouldin index, and Calinski–Harabasz index. Triangular and Gaussian membership functions have been investigated. The efficiency of the generated fuzzy rule bases has been tested on real datasets. The best
doi:10.21293/1818-0442-2022-25-2-31-36
fatcat:2t5s3mqa6fddflci6cq4me2y5a