A novel evolution learning for recurrent wavelet-based neuro-fuzzy networks

被引：0
|
作者：
Wang, DY [1 ]
Chuang, HC [1 ]
Xu, YJ [1 ]
Lin, CJ [1 ]
机构：
[1] Chaoyang Univ Technol, Dept Comp Sci & Informat Engn, Taichung 413, Taiwan
来源：
FUZZ-IEEE 2005: PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS: BIGGEST LITTLE CONFERENCE IN THE WORLD | 2005年
关键词：
genetic algorithms; neuro-fuzzy; symbiotic evolution; identification; control;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This study presents a recurrent wavelet-based neuro-fuzzy network with dynamic symbiotic evolution (RWNFN-DSE) model which combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). The proposed RWNFN-DSE is used to dynamic system processing. A novel evolution learning called dynamic symbiotic evolution (DSE) is used to tune the parameter of the RWN-N-DSE model. Better chromosomes will be initially generated while the better mutation points will be determined for performing dynamic-mutation. Simulation results have shown that the proposed RWVN-FN-DSE model obtains better performance than other existing models.
引用
收藏
页码：1092 / 1097
页数：6
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