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

This study presents a recurrent wavelet-based neuro-fuzzy network with dynamic symbiotic evolution (RWNFN-DSE) for dynamic system processing. The proposed RWNFN-DSE model combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions as wavelet neural network bases. Temporal relations embedded in the network are caused by adding some feedback connections representing the memory units into the second layer. A novel evolution learning called dynamic symbiotic evolution (DSE) is used to tune the parameter of the RWNFN-DSE model. The 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 obtain better performance than other existing models.