Learning Rule for TSK Fuzzy Logic Systems Using Interval Type-2 Fuzzy Subtractive Clustering

摘要

The paper deeds with an approach to model TSK fuzzy logic systems (FLS), especially interval type-2 TSK FLS, using interval type-2 fuzzy subtractive clustering (IT2-SC). The IT2-SC algorithm is combined with least square estimation (LSE) algorithms to pre-identify a type-1 FLS form from input/output data. Then, an interval type-2 TSK FLS can be obtained by considering the membership functions of its existed type-1 counterpart as primary membership functions and assigning uncertainty to cluster centroids, standard deviation of Gaussian membership functions and consequence parameters. Results is shown in comparison with the approach based on type-1 subtractive clustering algorithm.