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Constructing neuro-fuzzy systems with TSK fuzzy rules and hybrid SVD-based learning

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3 Author(s)
Wan-Jui Lee ; Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan ; Chen-Sen Ouyang ; Shie-Jue Lee

In this paper, an architecture of fuzzy neural networks with Takagi-Sugeno-Kang (TSK) fuzzy rules is proposed. A novel learning algorithm [1]-[2] with self-organizing ability and fast learning rules is also presented. In the structure identification phase of our method, fuzzy IF-THEN rules are extracted with a self-constructing rule generation algorithm. In the parameter identification phase, a hybrid learning algorithm is used, in which the consequent parameters are derived optimally by a recursive SVD-based least squares estimator (RSVD) and the precondition parameters are tuned by the backpropagation algorithm. Simulation results have demonstrated that a more compact structure with a faster convergence rate and smaller mean square errors can be achieved by the proposed approach

Published in:

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on  (Volume:2 )

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