Fuzzy Interpolative Reasoning for Sparse Fuzzy Rule-Based Systems Based on α-Cuts and Transformations Techniques

被引：69
作者：
Chen, Shyi-Ming [1 ,2 ]
Ko, Yuan-Kai [1 ]
机构：
[1] Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan
[2] Jinwen Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei, Taiwan
来源：
IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2008年 / 16卷 / 06期
关键词：
alpha-Cuts; fuzzy interpolative reasoning; increment transformations; ratio transformations; sparse fuzzy rule-based systems;
D O I：
10.1109/TFUZZ.2008.2008412
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In sparse fuzzy rule-based systems, the fuzzy rule bases are usually incomplete. In this situation, the system may not properly perform fuzzy reasoning to get reasonable consequences. In order to overcome the drawback of sparse fuzzy rule-based systems, there is an increasing demand to develop fuzzy interpolative reasoning techniques in sparse fuzzy rule-based systems. In this paper, we present a new fuzzy interpolative reasoning method via cutting and transformation techniques for sparse fuzzy rule-based systems. It can produce more reasonable results than the existing methods. The proposed method provides a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy rule-based systems.
引用
收藏
页码：1626 / 1648
页数：23
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