Can neural nets be universal approximators for fuzzy functions?

被引：0
|
作者：
Buckley, JJ
Hayashi, Y
机构：
来源：
PROCEEDINGS OF THE SIXTH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS I - III | 1997年
关键词：
neural nets; fuzzy functions; interval arithmetic; universal approximator;
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：
We first argue that the extension principle is too computationly involved to be an efficient way for a computer to evaluate fuzzy functions. We then suggest using a-cuts and interval arithmetic to compute the values of fuzzy functions. Using this method of computing fuzzy functions, we then show that neural nets are universal approximators for (computable) fuzzy functions, when we only input non-negative, or non-positive, fuzzy numbers.
引用
收藏
页码：1101 / 1104
页数：4
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