A wavelet-based neuro-fuzzy system and its applications
This paper addresses a Wavelet-Based Neuro-Fuzzy System (WNFS) for non-linear system identification and control. The proposed WNFS 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. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the degree measure to obtain the number of fuzzy rules and wavelet functions. Meanwhile, the parameter learning is based on the gradient descent method for adjusting the shape of the membership function and the connection weights of WNN. Several simulation examples have been given to illustrate the performance and effectiveness of the proposed model.
identification control wavelet neural networks tsk-type fuzzy model