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

被引：0

作者：

Oysal, Yusuf ^{[1
]}

Yilmaz, Sevcan ^{[1
]}

机构：

[1] Anadolu Univ, Dept Comp Engn, Eskisehir, Turkey

来源：

2013 10TH IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC) | 2013年

关键词：

Wavelet Neural Networks; ANFIS; Fuzzy Systems; Time Series Prediction; DYNAMICAL-SYSTEMS; IDENTIFICATION; PREDICTION; MODELS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：152 / 157

页数：6

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