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

被引：2

作者：

Ko, Yuan-Kai ^{[1
]}

Chen, Shyi-Ming ^{[1
,2
]}

Pan, Jeng-Shyang ^{[3
]}

机构：

[1] Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei, Taiwan

[2] Jinwen Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei, Taiwan

[3] Natl Kaohsiung Univ Appl Sci, Dept Elect Engn, YY Kaohsiung, Taiwan

来源：

PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2008年

关键词：

weighted fuzzy interpolative reasoning; sparse fuzzy rule-based systems; weighted increment transformations; weighted ratio transformations; alpha-cuts and transformation techniques;

D O I：

10.1109/ICMLC.2008.4621031

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 a-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.

引用

页码：3613 / +

页数：2

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