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一种基于支持向量回归的混合建模方法

孙泽斌 赵琦 赵洪博 冯文全 张文峰 杨天社

孙泽斌, 赵琦, 赵洪博, 等 . 一种基于支持向量回归的混合建模方法[J]. 北京航空航天大学学报, 2017, 43(2): 352-359. doi: 10.13700/j.bh.1001-5965.2016.0319
引用本文: 孙泽斌, 赵琦, 赵洪博, 等 . 一种基于支持向量回归的混合建模方法[J]. 北京航空航天大学学报, 2017, 43(2): 352-359. doi: 10.13700/j.bh.1001-5965.2016.0319
SUN Zebin, ZHAO Qi, ZHAO Hongbo, et al. An SVR based hybrid modeling method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(2): 352-359. doi: 10.13700/j.bh.1001-5965.2016.0319(in Chinese)
Citation: SUN Zebin, ZHAO Qi, ZHAO Hongbo, et al. An SVR based hybrid modeling method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(2): 352-359. doi: 10.13700/j.bh.1001-5965.2016.0319(in Chinese)

一种基于支持向量回归的混合建模方法

doi: 10.13700/j.bh.1001-5965.2016.0319
基金项目: 

国家“973”计划 

详细信息
    作者简介:

    孙泽斌, 男, 博士研究生。主要研究方向:故障诊断、故障预测、图像处理

    赵琦, 女, 博士, 副教授。主要研究方向:故障诊断、通信信号处理、认知无线电

    冯文全, 男, 博士, 教授, 博士生导师。主要研究方向:故障诊断、通信信号处理、卫星导航信号处理

    张文峰, 男, 博士研究生。主要研究方向:故障诊断、故障预测

    通讯作者:

    赵洪博, 男, 博士, 讲师。主要研究方向:故障诊断、卫星导航信号处理, E-mail:bhzhb@buaa.edu.cn

  • 中图分类号: TB114.3

An SVR based hybrid modeling method

Funds: 

National Basic Research Program of China 

More Information
  • 摘要:

    近年来,随着计算能力的不断提高,数据驱动的建模方法受到了广泛的关注,对单模式系统进行定量分析的建模方法获得了诸多研究。然而,实际应用中大多数系统为多模式系统,不但各个模式有着不同的连续行为,连续状态还会在模式之间进行切换。针对这一情形,本文提出了经验概率混合自动机模型,并提出了针对该模型的基于支持向量回归(SVR)的多模式定性定量混合建模方法。该方法使用小波技术识别模式切换点,并在各个模式下单独建立支持向量模型,最后使用D-Markov机整合模型。经实例验证,该方法与传统支持向量回归模型的稳定性接近,但精确程度显著提高。

     

  • 图 1  慢时间尺度与快时间尺度

    Figure 1.  Slow time scale and fast time scale

    图 2  混合建模框图

    Figure 2.  Block diagram of hybrid modeling

    图 3  混合系统行为

    Figure 3.  Behavior of hybrid system

    图 4  模式切换点识别

    Figure 4.  Recognition of mode switching points

    图 5  EPHA模型与SVR模型系统行为曲线

    Figure 5.  System behavior curves of EPHA model and SVR model

    表  1  SVR与EPHA均方误差

    Table  1.   Mean square error of SVR and EPHA

    序号 均方误差
    SVR EPHA
    1 0.089 6 0.071 6
    2 0.094 5 0.067 9
    3 0.094 4 0.072 8
    4 0.091 6 0.073 3
    5 0.093 5 0.071 8
    6 0.089 6 0.072 2
    7 0.091 2 0.070 1
    8 0.094 2 0.071 6
    9 0.093 5 0.068 7
    10 0.094 5 0.072 2
    方差 3.95×10-6 3.11×10-6
    均值 0.092 7 0.071 2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-04-19
  • 录用日期:  2016-04-29
  • 网络出版日期:  2017-02-20

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