A fast learning algorithm based on extreme learning machine for regular fuzzy neural network

被引：8
|
作者：
He, Chunmei [1 ,2 ]
Liu, Yaqi [1 ]
Yao, Tong [1 ]
Xu, Fanhua [1 ]
Hu, Yanyun [1 ]
Zheng, Jinhua [1 ]
机构：
[1] Xiangtan Univ, Coll Informat Engn, Xiangtan 411105, Peoples R China
[2] NUST, Coll Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China
来源：
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2019年 / 36卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Regular fuzzy neural network; learning algorithm; extreme learning machine;
D O I：
10.3233/JIFS-18046
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The regular fuzzy neural network (RFNN) is a kind of fuzzy neural network by fuzzifying the feed-forward neural network. The RFNN can directly deal with the language information and it has the merits of fuzzy system and neural network. It is presented a fast learning algorithm based on the extreme learning machine (ELM) for the RFNN in this paper. The RFNN referred here is a three-layer feed-forward fuzzy neural network and the connected weights in the RFNN are all fuzzy numbers. A simulation example is given to approximately realize the fuzzy if-then rules by the RFNN. The results show that the RFNN trained by the proposed algorithm has good performance and approximation ability.
引用
收藏
页码：3263 / 3269
页数：7
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