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

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2 Author(s)
Shyi-Ming Chen ; Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R. O. C. ; Yu-Chuan Chang

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.

Published in:

Fuzzy Systems (FUZZ), 2011 IEEE International Conference on

Date of Conference:

27-30 June 2011