Efficient method of fuzzy rules generation

被引：0
|
作者：
Wang, J [1 ]
Shen, L [1 ]
Chao, JF [1 ]
机构：
[1] Chinese Acad Sci, Comp Technol Inst, Beijing 100080, Peoples R China
来源：
1997 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT PROCESSING SYSTEMS, VOLS 1 & 2 | 1997年
关键词：
K-Nearest-Neighbor; essential point; fuzzy partition; fuzzy rules generation; BP algorithm; fuzzy neural network;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a method of automatic generation of fuzzy rules that finds out the essential points of the control surface by the concept of K-Nearest-Neighbor; and then uses these points to determine the fuzzy partitions so that it can construct a fuzzy neural network to learn fuzzy rules. The learning algorithm of the neural network is BP algorithm. During the training, the network can add new fuzzy partitions properly due to the condition of the convergence, and then reconstructs itself to learn again. This method can generate a simple and effective rule set, and has a good convergent condition as well as a fast convergent speed.
引用
收藏
页码：295 / 299
页数：5
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