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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

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
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)

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
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

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

     

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出版历程
  • 收稿日期:  2015-03-31
  • 网络出版日期:  2016-03-20

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