Extracting fuzzy if-then rules by using the information matrix technique

被引：42
|
作者：
Huang, CF [1 ]
Moraga, C
机构：
[1] Beijing Normal Univ, Inst Resources Technol & Engn, Coll Resources Sci & Technol, Beijing 100875, Peoples R China
[2] Univ Dortmund, Dept Comp Sci, D-44221 Dortmund, Germany
来源：
JOURNAL OF COMPUTER AND SYSTEM SCIENCES | 2005年 / 70卷 / 01期
关键词：
fuzzy rule; information matrix; information diffusion; additive fuzzy system; nonlinear function;
D O I：
10.1016/j.jcss.2004.05.001
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 use the information matrix technique to extract fuzzy if-then rules from data including noise. With a normal diffusion function, we change all crisp observations of a given sample into fuzzy sets to make an information matrix. We extract rules according to the centroids of the rows of an information matrix. These rules are integrated into an additive fuzzy system with the same rule weight. Such fuzzy systems can be used as adaptive function approximators. Simulations show that this method is very effective compared with the conventional least-squares method and neural network. The best advantage of the suggested method is that, it may be the simplest way to extract fuzzy if then rules from data. (C) 2004 Elsevier Inc. All rights reserved.
引用
收藏
页码：26 / 52
页数：27
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