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Volume 39 Issue 7
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Journal of Beijing University of Aeronautics and Astronautics  > 2013  >  39(7): 973-977.
Zhang Wei, Mao Jianqin. Least Wilcoxon learning method based fuzzy tree model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(7): 973-977. (in Chinese)
Citation: Zhang Wei, Mao Jianqin. Least Wilcoxon learning method based fuzzy tree model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(7): 973-977. (in Chinese)
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Least Wilcoxon learning method based fuzzy tree model

  • School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 28 Aug 2012
  • Publish Date: 30 Jul 2013
    Abstract
  • Fuzzy tree (FT) method used the least square method to learn the consequent parameters of the fuzzy rules, so it was sensitive to the outliers. The least Wilcoxon learning method was used to replace the least square method and a robust modeling method against (or insensitive to) outliers was proposed based on the least Wilcoxon learning method, called least Wilcoxon-fuzzy tree (LW-FT). The proposed method is not only insensitive to the outliers, but also has the advantages of the FT. Finally, the simulations on Mackey-Glass chaotic time series prediction were performed. The results show that the chaotic time series are accurately predicted, which demonstrates the effectiveness and the robustness to the outliers of this method.

     

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