Interval Type-2 Recursive Fuzzy C-Means Clustering Algorithm in the TS Fuzzy Model Identification

This paper presents an iterative Takagi Sugeno Fuzzy Model (TSFM) identification. Interval Type-2 Recursive Fuzzy C-Means (IT2RFCM) clustering algorithm has been used to classify the data space to obtain premise variable parameters and Weighted Recursive Least Square (WRLS) technique has been used to determine consequence parameters of each linear model. IT2RFCM clustering algorithm has been obtained from type-1 Fuzzy C-Means clustering algorithm by introducing fuzziness parameters. The effectiveness of the proposed IT2RFCM algorithm has been validated on Mackey-Glass time series data.