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
收藏
页码：3059 / +
页数：2
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