A wavelet-based neuro-fuzzy system and its applications
Cheng‐Jian Lin, Cheng‐Chung Chin, Cheng‐Ling Lee · 2004
This paper addresses a wavelet-based neuro-fuzzy system (WNFS) for non-linear system identification and control. The WNFS combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural network (WNN). Each fuzzy rule corresponding to a WNN consists of single-scaling wavelets. We adopt the non-orthogonal and compactly supported functions as wavelet neural network bases. The on-line structure/parameter learning algorithm is performed concurrently in the WNFS. The several simulation examples have been given to illustrate the performance and effectiveness of the proposed model.