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基于随机森林的航天器电信号多分类识别方法

兰巍 贾素玲 宋世民 李可

兰巍, 贾素玲, 宋世民, 等 . 基于随机森林的航天器电信号多分类识别方法[J]. 北京航空航天大学学报, 2017, 43(9): 1773-1778. doi: 10.13700/j.bh.1001-5965.2016.0661
引用本文: 兰巍, 贾素玲, 宋世民, 等 . 基于随机森林的航天器电信号多分类识别方法[J]. 北京航空航天大学学报, 2017, 43(9): 1773-1778. doi: 10.13700/j.bh.1001-5965.2016.0661
LAN Wei, JIA Suling, SONG Shimin, et al. Multi-classification spacecraft electrical signal identification method based on random forest[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(9): 1773-1778. doi: 10.13700/j.bh.1001-5965.2016.0661(in Chinese)
Citation: LAN Wei, JIA Suling, SONG Shimin, et al. Multi-classification spacecraft electrical signal identification method based on random forest[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(9): 1773-1778. doi: 10.13700/j.bh.1001-5965.2016.0661(in Chinese)

基于随机森林的航天器电信号多分类识别方法

doi: 10.13700/j.bh.1001-5965.2016.0661
基金项目: 

航空科学基金 2012XX1043

中央高校基本科研业务费专项资金 YWF-16-HKXY-017

国家自然科学基金 61773039

详细信息
    作者简介:

    兰巍   女, 博士研究生; 主要研究方向:数据挖掘与机器学习

    贾素玲   女, 教授; 主要研究方向:信息系统

    宋世民   男, 高级工程师; 主要研究方向:卫星综合测试

    李可   男, 高级实验师; 主要研究方向:环境控制与计算机测试

    通讯作者:

    李可, E-mail:like@buaa.edu.cn

  • 中图分类号: V221+.3;TB553

Multi-classification spacecraft electrical signal identification method based on random forest

Funds: 

Aeronautical Science Foundation of China 2012XX1043

the Fundamental Research Funds for the Central Universities YWF-16-HKXY-017

National Natural Science Foundation of China 61773039

More Information
  • 摘要:

    针对航天器电特性信号数据存在数据量大、特征维数高、计算复杂度大和识别率低等问题,提出基于主成分分析(PCA)的特征提取方法和随机森林(RF)算法,对原始数据进行降维,提高计算效率和识别率,实现对航天器电信号数据的快速、准确识别分类。随机森林算法在处理高维数据上具有优越的性能,但是考虑到时间复杂度问题,利用主成分分析方法对数据进行压缩和降维,在保证准确率的同时提高了计算效率。实验结果表明:与其他算法相比,针对航天器电特性信号数据,本文方法在准确率、计算效率和稳定性等方面均显示出优异的性能。

     

  • 图 1  本文算法设计流程

    Figure 1.  Design flowchart of proposed algorithm

    图 2  PCA方法流程

    Figure 2.  Flowchart of PCA method

    图 3  RF算法流程

    Figure 3.  Flowchart of RF algorithm

    图 4  决策树结构

    Figure 4.  Structure of decision tree

    图 5  部分电特性数据的物理意义

    Figure 5.  Physical meaning of some electrical characteristic data

    图 6  决策树数目与分类误差率曲线

    Figure 6.  Curves for number of decision trees and classification error rate

    表  1  训练时间和预测准确率对比

    Table  1.   Comparison of training time and prediction accuracy

    算法 准确率/% 训练时间/s
    NBM 79.02
    KNN 85.43 127.36
    SVM 88.23 1 873.80
    RF 98.90 189.93
    PCA-NBM 81.41
    PCA-KNN 94.34 11.33
    PCA-SVM 91.59 29.32
    PCA-RF 98.33 36.40
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-08-15
  • 录用日期:  2016-12-09
  • 网络出版日期:  2017-09-20

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