Weighted fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on transformation techniques

Author

Ko, Yuan-kai ; Chen, Shyi-Ming ; Pan, Jeng-Shyang

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei

Volume

6

fYear

2008

fDate

12-15 July 2008

Firstpage

3613

Lastpage

3618

Abstract

In this paper, we present a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. For multiple antecedent variables interpolation, the proposed method allows each condition appearing in the antecedent parts of fuzzy rules associated with a weighting factor. The alpha-cuts and transformation techniques are extended to handle the weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. The proposed method provides us a useful way to deal with weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

Keywords

fuzzy reasoning; interpolation; knowledge based systems; alpha-cuts; multiple antecedent variables interpolation; sparse fuzzy rule-based systems; transformation techniques; weighted fuzzy interpolative reasoning; Computer science; Cybernetics; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Interpolation; Knowledge based systems; Linear approximation; Machine learning; Multidimensional systems; α-cuts and transformation techniques; Weighted fuzzy interpolative reasoning; sparse fuzzy rule-based systems; weighted increment transformations; weighted ratio transformations;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4621031

Filename

4621031