@inproceedings{c56baf8743cc407389e40a19faf36978,
title = "Support Vector-trained Recurrent Fuzzy System",
abstract = "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.",
author = "Chung, {I. Fang} and Juang, {Chia Feng} and Hsieh, {Cheng Da}",
year = "2010",
doi = "10.1109/FUZZY.2010.5584494",
language = "English",
isbn = "9781424469208",
series = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010",
booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010",
note = "null ; Conference date: 18-07-2010 Through 23-07-2010",
}