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基于最小Wilcoxon学习方法的模糊树模型

张伟 毛剑琴

张伟, 毛剑琴. 基于最小Wilcoxon学习方法的模糊树模型[J]. 北京航空航天大学学报, 2013, 39(7): 973-977.
引用本文: 张伟, 毛剑琴. 基于最小Wilcoxon学习方法的模糊树模型[J]. 北京航空航天大学学报, 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)

基于最小Wilcoxon学习方法的模糊树模型

基金项目: 国家自然科学基金重点资助项目(91016006,91116002); 中央高校基本科研业务费专项资金资助项目
详细信息
  • 中图分类号: TP 13

Least Wilcoxon learning method based fuzzy tree model

  • 摘要: 模糊树方法采用最小二乘法学习模糊规则的后件参数,对例外点敏感.为此采用对例外点不敏感的最小Wilcoxon学习方法代替最小二乘法,提出一种基于最小Wilcoxon学习方法的模糊树建模方法,该方法既改善了模糊树方法对例外点敏感的缺点,又继承了模糊树方法的优点.通过对混沌时间序列预测研究,仿真结果表明:所提方法可以对Mackey-Glass混沌时间序列进行准确预测,验证了该方法的有效性和对例外点的鲁棒性.

     

  • [1] 张其光,王执铨.基于遗传算法的模糊神经网络控制器设计及其稳定性分析[J].控制理论与应用,1999,16(5):767-769 Zhang Qiguang,Wang Zhiquan.Design of a kind of fuzzy neural network controllers based on genetic algorithm and analysis of its stability [J].Control Theory and Applications,1999,16(5):767-769 (in Chinese)
    [2] Wang Yin,Rong Gang.A self-organizing neural-network-based fuzzy system[J].Control Theory and Applications,1999,16(3):455-457
    [3] 李波,张世英.基于神经模糊方法的复杂系统建模[J].信息与控制,2001,30(3):231-233 Li Bo,Zhang Shiying.Complex systems modeling via neuro fuzzy method[J].Information and Control,2001,30(3):231-233(in Chinese)
    [4] 王宏伟,杨振强,王子才.基于加速进化规划方法的复杂系统模糊建模[J].电子学报,1999,27(8):140-141 Wang Hongwei,Yang Zhenqiang,Wang Zicai.The fuzzy modeling of complex systems via accelerated evolutionary programming[J].Acta Electronica Sinica,1999,27(8):140-141 (in Chinese)
    [5] Carpenter G A,Grossberg S,Rosen D B.Fuzzy ART:fast stable learning and categorization of analog pattern by an adaptive resonance system[J].Neural Network,1991,4(9):759-771
    [6] Jang J S R.ANFIS:Adaptive-network-based fuzzy inference systems[J].IEEE Transactions on Systems,Man and Cybernetics,1993,23(3):665-685
    [7] Chiu S.Fuzzy model identification based on cluster estimation[J].Journal of Intelligent and Fuzzy Systems,1994,2(3):267-278
    [8] The Math Works Inc.Fuzzy logic toolbox for use with matlab user’s guide,Version 2 [M].USA:The Math Works Inc,2000
    [9] 毛剑琴,姚健,丁海山.基于模糊树模型的混沌时间序列预测[J].物理学报,2009,58(4):2220-2231 Mao Jianqin,Yao Jian,Ding Haishan.Chaotic time series prediction based on fuzzy tree[J].Acta Physica Sinica,2009,58(4):2220-2231 (in Chinese)
    [10] Mao Jianqin,Zhang Jiangang,Yue Yufang,et al.Adaptive tree-structured-based fuzzy Inference systems[J].IEEE Transactions on Fuzzy Systems,2005,13(1):1-12
    [11] Mao Jianqin,Ding Haishan.Intelligent modeling and control for nonlinear systems with rate-dependent hysteresis[J].Science in China Series F:Information Sciences,2009,52(4):656-673
    [12] Chuang C C,Su S F,Chen S S.Robust TSK fuzzy modeling for function approximation with outliers[J].IEEE Transactions on Fuzzy Systems,2001,9(6):810-821
    [13] Jacek M L.TSK-fuzzy modeling based onε-insensitive learning[J].IEEE Transactions on Fuzzy Systems,2005,13(2):181-193
    [14] Hogg R V,McKean J W,Craig A T.Introduction to mathematical statistics[M].6th ed.Englewood Cliffs,NJ:Prentice-Hall,2005
    [15] Hsieh J G,Lin Y L,Jeng J H.Preliminary study on Wilcoxon learning machines[J].IEEE Transactions on Neural Network,2008,19(2):201-211
    [16] Sun T Y,Tsai S J,Tsai C H,et al.Nonlinear function approximation based on least Wilcoxon takagi-sugeno fuzzy model //The Eighth International Conference on Intelligent Systems Design and Applications.Taiwan:IEEE Computer Society,2008:312-317
    [17] Majhi B,Panda G.Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique[J].Expert Systems with Applications,2010,38(1):321-333
    [18] Majhi B,Panda G,Mulgrew B.Robust identification and prediction using wilcoxon norm and particle swarm optimization //The 17th European Signal Processing Conference (EUSIPCO 2009).Scotland:European Association for Signal Processing,2009:1695-1699
    [19] Hardy G H,Littlewood J E,Polya G.Inequalities[M].2nd ed.Cambridge:Cambridge University Press,1952
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出版历程
  • 收稿日期:  2012-08-28
  • 网络出版日期:  2013-07-30

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