Support Vector-trained Recurrent Fuzzy System

I. Fang Chung*, Chia Feng Juang, Cheng Da Hsieh

*此作品的通信作者

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

This paper proposes a Support Vector-trained Recurrent Fuzzy System (SV-RFS) which comprises recurrent Takagi- Sugeno (TS) fuzzy if-then rules. The SV-RFS memories past input information by feeding the past firing strength of a fuzzy rule back to itself. The rules are generated based on a clustering-like algorithm. The feedback loop gains and consequent part parameters are learned through support vector regression (SVR) in order to improve system generalization ability. The SV-RFS is applied to noisy chaotic sequence prediction to verify its effectiveness.

原文English
主出版物標題2010 IEEE World Congress on Computational Intelligence, WCCI 2010
DOIs
出版狀態Published - 2010
事件2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - Barcelona, Spain
持續時間: 18 7月 201023 7月 2010

出版系列

名字2010 IEEE World Congress on Computational Intelligence, WCCI 2010

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010
國家/地區Spain
城市Barcelona
期間18/07/1023/07/10

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