Fuzzy temporal sequence processing by fuzzified recurrent neural fuzzy network

被引：0

作者：

Juang, CF ^{[1
]}

Ku, SJ ^{[1
]}

Huang, HJ ^{[1
]}

机构：

[1] Natl Chung Hsing Univ, Dept Elect Engn, Taichung 402, Taiwan

来源：

2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7 | 2004年

关键词：

fuzzy neural network; TSK-type fuzzy rule; temporal sequence prediction;

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：

A Fuzzified TSK-type Recurrent Neural Fuzzy Network (FTRNFN) for handling fuzzy temporal information is proposed in this paper. The inputs and outputs of FTRNFN are fuzzy patterns represented by Gaussian or isosceles triangular membership functions. In structure, FTRNFN is a recurrent fuzzy network constructed from a series of recurrent fuzzy if-then rules with TSK-type consequent parts. The recurrent proper, of FTRNFN enables it to deal with fizzy patterns with temporal context. There are no rules in FTRNFN initially; they are constructed on-line by concurrent structure and parameter learning. The ability of TRFNFN is verified from a two-dimensional fuzzy temporal sequence prediction problem.

引用

页码：5847 / 5851

页数：5

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