Title :
A Fuzzy Wavelet Neural Network Model for System Identification
Author :
Yilmaz, Sevcan ; Oysal, Yusuf
Author_Institution :
Comput. Eng. Dept., Anadolu Univ., Eskisehir, Turkey
fDate :
Nov. 30 2009-Dec. 2 2009
Abstract :
In this paper, a fuzzy wavelet neural network model is proposed for system identification problems. The proposed model is obtained from the traditional Takagi-Sugeno-Kang (TSK) fuzzy system by replacing the consequent part of fuzzy rules with wavelet basis functions that have time-frequency localization properties. We use a radial function of Mexican Hat wavelet in the consequent part of each rule. A fast gradient algorithm based on quasi-Newton methods is used to obtain the optimal values for unknown parameters of the model. Simulation results of some benchmark problems in the literature are also given to illustrate the effectiveness of the model.
Keywords :
Newton method; fuzzy neural nets; gradient methods; radial basis function networks; wavelet transforms; Mexican Hat wavelet; Takagi-Sugeno-Kang fuzzy system; fuzzy rule; fuzzy wavelet neural network; gradient algorithm; quasiNewton method; radial function; system identification; time-frequency localization property; wavelet basis function; Control systems; Design engineering; Discrete wavelet transforms; Function approximation; Fuzzy neural networks; Fuzzy systems; Intelligent systems; Neural networks; System identification; Time frequency analysis;
Conference_Titel :
Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on
Conference_Location :
Pisa
Print_ISBN :
978-1-4244-4735-0
Electronic_ISBN :
978-0-7695-3872-3
DOI :
10.1109/ISDA.2009.96
