高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于改进人工神经网络的航天器电信号分类方法

李可,  王全鑫,  宋世民,  孙毅,  王浚

downloadPDF
文章导航 > 北京航空航天大学学报  > 2016 >  42(3): 596-601.
李可, 王全鑫, 宋世民, 等 . 基于改进人工神经网络的航天器电信号分类方法[J]. 北京航空航天大学学报, 2016, 42(3): 596-601. doi: 10.13700/j.bh.1001-5965.2015.0186
引用本文: 李可, 王全鑫, 宋世民, 等 . 基于改进人工神经网络的航天器电信号分类方法[J]. 北京航空航天大学学报, 2016, 42(3): 596-601. doi: 10.13700/j.bh.1001-5965.2015.0186
shu
LI Ke, WANG Quanxin, SONG Shimin, et al. Spacecraft electrical signal classification method based on improved artificial neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(3): 596-601. doi: 10.13700/j.bh.1001-5965.2015.0186(in Chinese)
Citation: LI Ke, WANG Quanxin, SONG Shimin, et al. Spacecraft electrical signal classification method based on improved artificial neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(3): 596-601. doi: 10.13700/j.bh.1001-5965.2015.0186(in Chinese)
shu

基于改进人工神经网络的航天器电信号分类方法

doi: 10.13700/j.bh.1001-5965.2015.0186
  • 1.

    北京航空航天大学航空科学与工程学院, 北京 100083

  • 2. 中国空间技术研究院, 北京 100094

基金项目: 中央高校基本科研业务费专项资金(YWF-14-HKXY-017);航空科学基金(2012XX1043)
详细信息
    作者简介:

    李可 男,博士,讲师。主要研究方向:智能控制和模式识别。Tel.:13810609687 E-mail:like@buaa.edu.cn;王全鑫 男,硕士研究生。主要研究方向:高光谱数据挖掘。Tel.:13220115884 E-mail:xin20071261@sina.com

    通讯作者:

    李可,Tel.:13810609687 E-mail:like@buaa.edu.cn

  • 中图分类号: V557.1

Spacecraft electrical signal classification method based on improved artificial neural network

  • 1.

    School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • 2. China Academy of Space Technology, Beijing 100094, China

Funds: Aeronautical Science Foundation of China (2012XX1043);the Fundamental Research Funds for the Central Universities (YWF-14-HKXY-017)

    摘要
  • 摘要: 根据航天器系统级电性能测试工作中数据量大、任务繁重的特点,设计了基于人工神经网络的智能分类系统,对原始测试数据进行智能化分类,将非线性的调试经验以数据的形式储备,可在减少测试工作中对人为经验依赖的同时为航天器信号识别快速提供专家知识。考虑到经典的人工神经网络系统有训练时间长和对网络初始权值的依赖程度高等不足,利用主成分分析对数据进行压缩和自动编码技术对网络权值进行初始化。实验数据测试表明:与传统方法相比,本文提出的改进学习系统的分类准确率、稳定性和响应速度均得到显著提高。

     

    Abstract: To solve the problem of multiple data and arduous task in the aircraft test and intellectualize the management of the testing work, an intelligent classification system based on artificial neural networks was designed. The system can classify the original test data intelligently, reduce the workload and reliance on testing experience and store the nonlinear debugging experience in the form of expert database. This system has many deficiencies, such as, long training time and high dependence on the initial threshold. To this end, the principal component analysis was used to compress the raw data and auto-encoder in deep learning was applied to initialize the network weights. Experimental data indicates that compared with traditional methods, the accuracy, stability and response speed of the improved learning system are significantly increased.

     

    • HTML全文
    • 参考文献(17)
  • [1] LI K,LIU W K, WANG J,et al.An intelligent control method for a large multi-parameter environmental simulation cabin[J].Chinese Journal of Aeronautics,2013,26(6):1360-1369.
    [2] LIU Y,LI K, SONG S,et al.The research of spacecraft electrical characteristics identification and diagnosis using PCA feature extraction signal processing[C]//International Conference on Signal Processing(ICSP).Piscataway,NJ:IEEE Press,2014:1413-1417.
    [3] LIU Y,LI K, HUANG Y,et al.Spacecraft electrical characteristics identification study based on offline FCM clustering and online SVM classifier[C]//Multisensor Fusion and Information Integration for Intelligent Systems(MFI).Piscataway,NJ:IEEE Press,2014:1-4.
    [4] LI K,LIU W K, WANG J,et al.Multi-parameter decoupling and slope tracking control strategy of a large-scale high altitude environment simulation test cabin[J].Chinese Journal of Aeronautics,2014,27(6):1390-1400.
    [5] 杨天社,杨开忠, 李怀祖.基于知识的卫星故障诊断与预测方法[J].中国工程科学,2003,5(6):64-67. YANG T S,YANG K Z,LI H Z.Research on knowledge-based method for satellite fault diagnosis and prediction[J].Engineering Science,2003,5(6):64-67(in Chinese).
    [6] 韩立群. 人工神经网络[M].北京:北京邮电大学出版社,2006:20-66. HAN L Q.Artificial neural networks[M].Beijing:Beijing University of Posts and Telecommunications Press,2006:20-66(in Chinese).
    [7] 冯伟兴,梁洪, 王臣业.Visual C++数字图像模式识别典型案例详解[M].北京:机械工业出版社,2012:50-63. FENG W X,LIANG H,WANG C Y.Visual C++digital image pattern recognition detailed typical case[M].Beijing:China Machine Press,2012:50-63(in Chinese).
    [8] 秦振汉. 人工神经网络专家系统在卫星故障诊断中的应用研究[D].哈尔滨:哈尔滨工业大学,2005:8-12. QIN Z H.Application of neural network expert system in satellite fault diagnosis[D].Harbin:Harbin Institute of Technology,2005:8-12(in Chinese).
    [9] HUANG H, ZHU Y W,YANG L P,et al.Stability and shape analysis of relative equilibrium for three-spacecraft electromagnetic formation[J].Acta Astronautica,2014,94(1):116-131.
    [10] KENIG S, BEN-DAVID A,OMER M,et al.Control of properties in injection molding by neural networks[J].Engineering Applications of Artificial Intelligence,2001,14(6):819-823.
    [11] 戴文战,娄海川, 杨爱萍.非线性系统人工神经网络预测控制研究进展[J].控制理论与应用,2009,26(5):521-530. DAI W Z,LOU H C,YANG A P,et al.An overview of neural network predictive control for nonlinear systems[J].Control Theory & Applications,2009,26(5):521-530(in Chinese).
    [12] 姚健,纪志成,黄言平. 基于人工神经网络的非线性多模型自适应控制[J].控制工程,2014,21(2):172-177. YAO J,JI Z C,HUANG Y P.Nonlinear multi-model adaptive control based on neural networks[J].Control Engineering of China,2014,21(2):172-177(in Chinese).
    [13] LEE C Y,LEE J J. Adaptive control for uncertain nonlinear systems based on multiple neural networks[J].IEEE Transactions on Systems,Man and Cybernetics-Part B:Cybernetics,2004,34(1):325-333.
    [14] VRABIE D, LEWIS F.Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems[J].Neural Networks,2009,22(3):237-246.
    [15] 林海明,杜子芳. 主成分分析综合评价应该注意的问题[J].统计研究,2013,30(8):25-31. LIN H M,DU Z F.Some problems in comprehensive evaluation in the principal component analysis[J].Statistical Research,2013,30(8):25-31(in Chinese).
    [16] SCHÖLKOPF B, PLATT J,HOFMANN T.Greedy layer-wise training of deep networks[C]//Proceedings of the 2006 Conference, Advances in Neural Information Processing Systems. Cambridge:MIT Press,2007,19:153-160.
    [17] HINTON G E, OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554.
    • 相关文章
    • 施引文献
  • 资源附件(0)
  • 加载中
WeChat 点击查看大图
计量
  • 文章访问数:  997
  • HTML全文浏览量:  79
  • PDF下载量:  544
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-03-31
  • 网络出版日期:  2016-03-20

目录

    /

    返回文章
    返回
    常见问答

    版权所有 © 《北京航空航天大学学报》编辑部

    通讯地址：北京市海淀区学院路37号 《北京航空航天大学学报》编辑部　 邮编：100191 邮箱：jbuaa@buaa.edu.cn

    本系统由 北京仁和汇智信息技术有限公司 开发    百度统计