A designing method for type-2 fuzzy logic systems using genetic algorithms

Fuzzy logic systems (FLSs) have been successfully used in widely various applications. The membership functions (MFs) and the rules of an FLS are designed using the linguistic information or numeric data. However, there is uncertainty associated with the information or data. A type-2 fuzzy set can represent and handle uncertain information effectively. Type-2 fuzzy sets are used to incorporate uncertainty in type-2 FLSs. To design a type-2 FLS, the optimization of both the MFs and the rules is required. Genetic algorithms (GAs) are known to have a strong optimizing capability as they search the solution space in parallel. GAs have been used to design the type-1 FLSs. We propose a design method for a type-2 FLS using GAs. The proposed method determines the positions and the shapes of the MFs and the rules of a type-2 FLS. We encode type-2 fuzzy sets as feature parameters. The proposed method is applied to the chaotic time-series prediction and the result of the experiment is shown to demonstrate the performance