An Adaptive Fuzzy Wavelet Neural Network with Gradient Learning Algorithm For Nonlinear Function Approximation

Oysal Y., Yilmaz S.

10th IEEE International Conference on Networking, Sensing and Control (ICNSC), France, 10 - 12 April 2013, pp.152-157 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/icnsc.2013.6548728
  • Country: France
  • Page Numbers: pp.152-157
  • Keywords: Wavelet Neural Networks, ANFIS, Fuzzy Systems, Time Series Prediction, DYNAMICAL-SYSTEMS, IDENTIFICATION, PREDICTION, MODELS
  • Anadolu University Affiliated: Yes

Abstract

In this paper a new adaptive fuzzy wavelet neural network (AFWNN) model is proposed for nonlinear function approximation problems. The AFWNN model is a Takagi-Sugeno-Kang (TSK) fuzzy system in which the membership functions of fuzzy rules are replaced with wavelet basis functions, which are known to have time and frequency localization properties. The AFWNN model is trained using a gradient-based optimization algorithm for certain types of nonlinear time series, for instance fractal processes and the simulation results are found to be substantially more accurate than alternative methods.