Extracting Interpretable Fuzzy Rules from RBF Networks

被引：0
|
作者：
Yaochu Jin
Bernhard Sendhoff
机构：
[1] Honda Research Institute Europe,
来源：
Neural Processing Letters | 2003年 / 17卷
关键词：
Neural Network; Artificial Intelligence; Basis Function; Complex System; Artificial Neural Network;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Radial basis function networks and fuzzy rule systems are functionally equivalent under some mild conditions. Therefore, the learning algorithms developed in the field of artificial neural networks can be used to adapt the parameters of fuzzy systems. Unfortunately, after the neural network learning, the structure of the original fuzzy system is changed and interpretability, which is considered to be one of the most important features of fuzzy systems, is usually impaired. This Letter discusses the differences between RBF networks and interpretable fuzzy systems. Based on these discussions, a method for extracting interpretable fuzzy rules from RBF networks is suggested. Simulation examples are given to embody the idea of this paper.
引用
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页码：149 / 164
页数：15
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