Fuzzy Rules Interpolation for Sparse Fuzzy Rule-Based Systems Based on Interval Type-2 Gaussian Fuzzy Sets and Genetic Algorithms

Author

Chen, Shyi-Ming ; Chang, Yao-Chung ; Pan, Jeng-Shyang

Author_Institution

Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

Volume

21

Issue

3

fYear

2013

fDate

Jun-13

Firstpage

412

Lastpage

425

Abstract

In this paper, we present a new method for fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 Gaussian fuzzy sets and genetic algorithms. First, we present a method to deal with the interpolation of fuzzy rules based on interval type-2 Gaussian fuzzy sets. We also prove that the proposed method guarantees to produce normal interval type-2 Gaussian fuzzy sets. Then, we present a method to learn optimal interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems based on genetic algorithms. We also apply the proposed fuzzy rules interpolation method and the proposed learning method to deal with multivariate regression problems and time series prediction problems. The experimental results show that the proposed fuzzy rules interpolation method using the optimally learned interval type-2 Gaussian fuzzy sets gets higher average accuracy rates than the existing methods.

Keywords

Fuzzy sets; Genetic algorithms; Interpolation; Learning systems; Multivariate regression; Standards; Time series analysis; Fuzzy rules interpolation; genetic algorithms; interval type-2 Gaussian fuzzy sets; sparse fuzzy rule-based systems;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/TFUZZ.2012.2226942

Filename

6342907