Resilient back propagation learning algorithm for recurrent fuzzy neural networks

被引：17

作者：

Mastorocostas, PA ^{[1
]}

机构：

[1] Technol & Educ Inst Serres, Dept Informat & Commun, Serres 62124, Greece

来源：

ELECTRONICS LETTERS | 2004年 / 40卷 / 01期

关键词：

D O I：

10.1049/el:20040052

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

An efficient training method for recurrent fuzzy neural networks is proposed. The method modifies the RPROP algorithm, originally developed for static neural networks, in order to be applied to dynamic systems. A comparative analysis with the standard back propagation through time is given, indicating the effectiveness of the proposed algorithm.

引用

页码：57 / 58

页数：2

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