Generating weighted fuzzy production rules using neural networks

被引：0

作者：

Fan, Tie-Gang ^{[1
]}

Wang, Shu-Tian ^{[2
]}

Chen, Jun-Min ^{[1
]}

机构：

[1] Hebei Univ, Fac Math & Comp Sci, Baoding 071002, Peoples R China

[2] Hebei Inst Ind Technol, Dept Basic Sci, Shijiazhuangcc 050091, Peoples R China

来源：

PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年

基金：

中国国家自然科学基金;

关键词：

weighted fuzzy production rules; neural networks; rules extraction; reasoning algorithm;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Weighted fuzzy production rules enhance the knowledge representation power of rule. This paper proposes a way to generate weighted fuzzy production rules using neural networks. First the knowledge in the data is transformed into neural network. Through analysis of the weights of the neural network, a matrix of importance index is constructed. Then weighted fuzzy production rules are extracted from the neural network. In order to reflect the knowledge implied in the neural network accurately, a corresponding reasoning algorithm is constructed. The effective of the approach is demonstrated by the experiment..

引用

页码：3059 / +

页数：2

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