A gradient descent learning algorithm for fuzzy neural networks

被引：0

作者：

Feuring, T ^{[1
]}

Buckley, JJ ^{[1
]}

Hayashi, Y ^{[1
]}

机构：

[1] Univ Munster, D-4400 Munster, Germany

来源：

1998 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AT THE IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - PROCEEDINGS, VOL 1-2 | 1998年

关键词：

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：

In order to train fuzzy neural nets fuzzy number weights have to be adjusted. Because fuzzy arithmetic automatically leads to monotonic increasing outputs a direct fuzzification of the backpropagation method does not work. Therefore, other strategies like evolutionary algorithms are focused on in the literature [1], [2]. In this paper we suggest a backpropagation based method of adjusting the weights. Furthermore we can show that for the proposed method convergence can be guaranteed.

引用

页码：1136 / 1141

页数：6

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