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Volume 42 Issue 3
Mar.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(3): 596-601.
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

Spacecraft electrical signal classification method based on improved artificial neural network

doi: 10.13700/j.bh.1001-5965.2015.0186
  • 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)
  • Received Date: 31 Mar 2015
  • Publish Date: 20 Mar 2016
    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.

     

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