Adjusting fuzzy weights in fuzzy neural nets

被引：0
|
作者：
Feuring, T [1 ]
Buckley, JJ [1 ]
Hayashi, Y [1 ]
机构：
[1] Univ Munster, Inst Informat, D-48149 Munster, Germany
来源：
1998 SECOND INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT ELECTRONIC SYSTEMS, KES '98, PROCEEDINGS, VOL 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]. In this paper we suggest a backpropagtion based method of adjusting the weights. Furthermore we can show that for the proposed method convergence can be guaranteed.
引用
收藏
页码：402 / 406
页数：5
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