A Designing Method for Type-2 Fuzzy Logic Systems Using Genetic Algorithms

lar structure except type-reducer, comparing to that of Fuzzy logic systems(FLSs) have been successfully used in widely various applications. The membership functions(MFs) and the rules of a 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. Recently, 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 searching the solution space in parallel. GAS have been used to design the type-1 FLSs. In this paper, we propose a designing 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.