Successive Optimization of Interval Type-2 Fuzzy C-Means Clustering Algorithm-based Fuzzy Inference Systems
نویسندگان
چکیده
A design methodology of interval type-2 fuzzy c-means clustering algorithm-based fuzzy inference systems (IT2FCMFIS) is introduced in this paper. An interval type-2 fuzzy c-means (IT2FCM) clustering algorithm is developed to generate the fuzzy rules in the form of the scatter partition of input space. And the individual partitioned spaces describe the fuzzy rules equal to the number of clusters. The consequence part of the rule is represented by polynomial functions with interval set. To optimally construct of fuzzy model we exploit realcoded genetic algorithms with successive optimization. The proposed model is evaluated through the numeric experimentation.
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