FUNCTIONAL EQUIVALENCE BETWEEN RADIAL BASIS FUNCTION NETWORKS AND FUZZY INFERENCE SYSTEMS

被引：526

作者：

JANG, JSR

SUN, CT

机构：

[1] Department of Electrical and Computer Science, University of California, Berkeley

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 1993年 / 4卷 / 01期

关键词：

D O I：

10.1109/72.182710

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This letter shows that under some minor restrictions, the functional behavior of radial basis function networks (RBFN's) and fuzzy inference systems are actually equivalent. This functional equivalence enables us to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functional equivalent.

引用

页码：156 / 159

页数：4

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