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  2010, Vol. 23 Issue (6): 874-879    DOI:
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Variable Structure Radial Basis Function Network and Its Application to On-Line Chaotic Time Series Prediction
YIN Jian-Chuan,HU Jiang-Qiang,HE Qing-Hua
College of Navigation,Dalian Maritime University,Dalian 116026

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Abstract  

To improve the accuracy and the speed of on-line chaotic time series prediction via radial basis function (RBF) network, a sequential learning algorithm is presented for on-line constructing variable structure RBF network. A sliding window is constructed. By learning real-time updated data in the window, the parameters of the connecting weights, number of hidden units and center locations are dynamically tuned. The algorithm achieves parsimonious RBF network quickly, while only a small number of tuning parameters are employed. The variable structure network is applied to Mackey-Glass chaotic time series on-line prediction. The results demonstrate that network possesses satisfactory on-line dynamic identification and prediction performance.

Key wordsChaotic Time Series Prediction      Radial Basis Function Network      Sequential Learning      Sliding Data Window     
Received: 05 June 2009     
ZTFLH: TP183  
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YIN Jian-Chuan
HU Jiang-Qiang
HE Qing-Hua
Cite this article:   
YIN Jian-Chuan,HU Jiang-Qiang,HE Qing-Hua. Variable Structure Radial Basis Function Network and Its Application to On-Line Chaotic Time Series Prediction[J]. , 2010, 23(6): 874-879.
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