Fuzzy rule interpolation based on interval type-2 Gaussian fuzzy sets and genetic algorithms

In this paper, we present a new method for fuzzy rule interpolation with interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems based on genetic algorithms. The proposed fuzzy rule interpolation method deals with the interpolation of fuzzy rules based on the multiple fuzzy rules interpolation scheme. We also present a new learning method to learn optimal interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems based on genetic algorithms. We apply the proposed fuzzy rule interpolation method and the proposed learning method to deal with the Mackey-Glass chaotic time series prediction problem. The experimental result shows that the proposed fuzzy rule interpolation method using the optimally learned interval type-2 Gaussian fuzzy sets obtained by the proposed learning method gets higher average accuracy rates than the existing methods to deal with the Mackey-Glass chaotic time series prediction problem.