Title :
A novel evolution learning for recurrent wavelet-based neuro-fuzzy networks
Author :
Wang, De-Yu ; Chuang, Ho-Chin ; Xu, Yong-Ji ; Lin, Cheng-Jian
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Chaoyang Univ. of Technol., Taichung
Abstract :
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 RWNFN-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 RWNFN-DSE model obtains better performance than other existing models
Keywords :
fuzzy neural nets; fuzzy set theory; fuzzy systems; genetic algorithms; learning (artificial intelligence); recurrent neural nets; wavelet transforms; RWNFN-DSE model; TSK fuzzy model; Takagi-Sugeno-Kang fuzzy model; dynamic mutation; dynamic symbiotic evolution; dynamic system processing; evolution learning; genetic algorithms; identification; mutation points; parameter tuning; recurrent wavelet-based neurofuzzy networks; wavelet neural networks; Biological cells; Computer science; Fuzzy neural networks; Fuzzy reasoning; Fuzzy systems; Genetic algorithms; Genetic mutations; Input variables; Neural networks; Symbiosis;
Conference_Titel :
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on
Conference_Location :
Reno, NV
Print_ISBN :
0-7803-9159-4
DOI :
10.1109/FUZZY.2005.1452547
