A type-2 fuzzy c-regression clustering algorithm for TakagiSugeno system identification and its application in the steel industry
This paper proposes a new type-2 fuzzy c-regression clustering algorithm for the structure identification phase of TakagiSugeno (TS) systems. We present uncertainties with fuzzifier parameter m . In order to identify the parameters of interval type-2 fuzzy sets, two fuzzifiers m1 m 1  and m2 m 2  are used. Then, by utilizing these two fuzzifiers in a fuzzy c-regression clustering algorithm, the interval type-2 fuzzy membership functions are generated. The proposed model in this paper is an extended version of a type-1 FCRM algorithm [25], which is extended to an interval type-2 fuzzy model. The Gaussian Mixture model is used to create the partition matrix of the fuzzy c-regression clustering algorithm. Finally, in order to validate the proposed model, several numerical examples are presented. The model is tested on a real data set from a steel company in Canada. Our computational results show that our model is more effective for robustness and error reduction than type-1 NFCRM and the multiple-regression.
steel industry structure identification gaussian mixture it2f c-regression clustering weighted least square multiple-regression