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基于量子万有引力搜索的SVM自驾故障诊断

李海涛 何玉珠 宋平

李海涛, 何玉珠, 宋平等 . 基于量子万有引力搜索的SVM自驾故障诊断[J]. 北京航空航天大学学报, 2016, 42(6): 1093-1098. doi: 10.13700/j.bh.1001-5965.2015.0417
引用本文: 李海涛, 何玉珠, 宋平等 . 基于量子万有引力搜索的SVM自驾故障诊断[J]. 北京航空航天大学学报, 2016, 42(6): 1093-1098. doi: 10.13700/j.bh.1001-5965.2015.0417
LI Haitao, HE Yuzhu, SONG Pinget al. SVM fault diagnosis of autopilot based on quantum inspired gravitational search algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(6): 1093-1098. doi: 10.13700/j.bh.1001-5965.2015.0417(in Chinese)
Citation: LI Haitao, HE Yuzhu, SONG Pinget al. SVM fault diagnosis of autopilot based on quantum inspired gravitational search algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(6): 1093-1098. doi: 10.13700/j.bh.1001-5965.2015.0417(in Chinese)

基于量子万有引力搜索的SVM自驾故障诊断

doi: 10.13700/j.bh.1001-5965.2015.0417
详细信息
    作者简介:

    李海涛 男,硕士研究生。主要研究方向:自动检测技术、故障诊断。E-mail:771084677@qq.com;何玉珠 男,博士,教授,博士生导师。主要研究方向:测试系统通用性技术、故障诊断和定位技术。Tel.:15811175527 E-mail:heyuzhuhe@buaa.edu.cn

    通讯作者:

    何玉珠,Tel.:15811175527 E-mail:heyuzhuhe@buaa.edu.cn

  • 中图分类号: TP277

SVM fault diagnosis of autopilot based on quantum inspired gravitational search algorithm

  • 摘要: 针对自动驾驶仪在实际测试过程中故障样本较少的情况,提出一种基于量子万有引力搜索算法(QGSA)的支持向量机(SVM)故障诊断模型。SVM能较好地解决小样本、非线性问题,适用于自动驾驶仪的故障诊断。为进一步提高万有引力搜索算法(GSA)对参数寻优的收敛速度和收敛精度,将基于GSA的QGSA应用于SVM的参数寻优中,以解决SVM由于参数选取不当导致过学习或欠学习的问题,从而获得最优的分类模型。通过模拟实验分析,当训练样本数量为50时,基于QGSA的SVM故障诊断模型分类准确率便能达到96.530 6%,而基于遗传算法(GA)的SVM故障诊断模型分类准确率为92.040 8%,基于GSA的SVM故障诊断模型分类准确率为91.632 7%。仿真实验结果表明,基于QGSA的SVM故障诊断模型具有更好的故障诊断能力。

     

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出版历程
  • 收稿日期:  2015-06-23
  • 网络出版日期:  2016-06-20

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