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Support Vector-trained Recurrent Fuzzy System

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3 Author(s)
I-Fang Chung ; Inst. of Biomed. Inf., Nat. Yang-Ming Univ., Taipei, Taiwan ; Chia-Feng Juang ; Cheng-Da Hsieh

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.

Published in:

Fuzzy Systems (FUZZ), 2010 IEEE International Conference on

Date of Conference:

18-23 July 2010