A new TSK fuzzy modeling approach
Kyoungjung Kim, You Keun Kim, Euntai Kim, Mignon Park · 2005
A new robust TSK fuzzy modeling algorithm is proposed. The proposed algorithm is the modified version of noise clustering algorithm. Various robust approaches to deal with the data containing noise or outliers in real applications were proposed, but most algorithms process clustering of data first and then conduct fuzzy regression. We propose the algorithm that parameters of the premise part and the consequent part are obtained simultaneously. The proposed algorithm shows good performance against noise or outliers. Without adaptation of parameters, the proposed algorithm shows the superior performance over other approaches.