Type-2 fuzzy logic systems

We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncer tainties, The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on "output proces sing," which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type redu ction captures more information about rule uncertainties than does the defu zzified value (a crisp number), however, it is computationally intensive, e xcept for interval type-2 fuzzy sets for which we provide a simple type-red uction computation procedure. We also apply a type-2 FLS to time-varying ch annel equalization and demonstrate that it provides better performance than a type-1 FLS and nearest neighbor classifier.