Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on the ranking values of fuzzy sets

被引：28

作者：

Lee, Li-Wei ^{[1
]}

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

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

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2008年 / 35卷 / 03期

关键词：

fuzzy interpolative reasoning; ranking values; ranking proportional interpolative coefficients; sparse fuzzy rule-based systems;

D O I：

10.1016/j.eswa.2007.07.027

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fuzzy interpolative reasoning is an important research topic of sparse fuzzy rule-based systems. In recent years, some methods have been presented for dealing with fuzzy interpolative reasoning. However, the involving fuzzy sets appearing in the antecedents of fuzzy rules of the existing fuzzy interpolative reasoning methods must be normal and non-overlapping. Moreover, the reasoning conclusions of the existing fuzzy interpolative reasoning methods sometimes become abnormal fuzzy sets. In this paper, in order to overcome the drawbacks of the existing fuzzy interpolative reasoning methods, we present a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the ranking values of fuzzy sets. The proposed fuzzy interpolative reasoning method can handle the situation of non-normal and overlapping fuzzy sets appearing in the antecedents of fuzzy rules. It can overcome the drawbacks of the existing fuzzy interpolative reasoning methods in sparse fuzzy rule-based systems. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：850 / 864

页数：15

共 24 条

[1]

Baranyi P, 1996, INFORMATION INTELLIGENCE AND SYSTEMS, VOLS 1-4, P510, DOI 10.1109/ICSMC.1996.569844

[2]

Baranyi P., 1999, P 1999 IEEE INT C FU, P383

[3]

Bouchon-Meunier B, 2000, NINTH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2000), VOLS 1 AND 2, P483, DOI 10.1109/FUZZY.2000.838707

[4] A new interpolative reasoning method in sparse rule-based systems [J].

Hsiao, WH ;

Chen, SM ;

Lee, CH .

FUZZY SETS AND SYSTEMS, 1998, 93 (01) :17-22

[5]

Huang DM, 2004, PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P1826

[6] Fuzzy interpolative reasoning via scale and move transformations [J].

Huang, ZH ;

Shen, Q .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2006, 14 (02) :340-359

[7]

Huang ZH, 2004, IEEE INT CONF FUZZY, P623

[8]

Huang ZH, 2003, IEEE INT CONF FUZZY, P25

[9] Size reduction by interpolation in fuzzy rule bases [J].

Koczy, LT ;

Hirota, K .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1997, 27 (01) :14-25

[10] INTERPOLATIVE REASONING WITH INSUFFICIENT EVIDENCE IN SPARSE FUZZY RULE BASES [J].

KOCZY, LT ;

HIROTA, K .

INFORMATION SCIENCES, 1993, 71 (1-2) :169-201

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