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SCIENTIA SINICA Informationis, Volume 48 , Issue 7 : 824-840(2018) https://doi.org/10.1360/N112017-00303

Backtracking analysis approach for effectiveness of air defense operation system of systems based on force-sparsed stacked-autoencoding neural networks

More info
  • ReceivedDec 31, 2017
  • AcceptedJan 28, 2018
  • PublishedJul 19, 2018

Abstract


Funded by

国家自然科学基金(61273189)

国家自然科学基金(71401168)

国家自然科学基金(61174156)

国家自然科学基金(61403401)

国家自然科学基金(61374179)

军民共用重大研究计划联合基金(U1435218)


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  • Figure 1

    (Color online) The idea of backtracking analysis approach based on FS-SAE

  • Figure 2

    (Color online) The design route for the structure of FS-SAE model. (a) Initial index sets; (b) initial index networks; (c) function indexes for SOS; (d) eigen indexes of community; (e) multilayered structure

  • Figure 3

    The flowchart of GN algorithm

  • Figure 4

    (Color online) Scenario setting and the construction of the networked index system of ADSOS. (a) Illustration of scenario setting; (b) initial index networks; (c) index network communities; (d) multilayer networked index system

  • Figure 5

    (Color online) Convergence comparison of different algorithms

  • Figure 6

    (Color online) The distribution of function index expectation of ADSOS. (a) The probability of function index expectation; (b) the range and the average value of function index expectation

  • Table 1   MICs between the initial indexes
    Index $X$1 $X$2 $X$3 $X$4 $X$5 $X$6
    $X$2 0.22279
    $X$3 0.24872 0.87671
    $X$4 0.23126 0.51958 0.71057
    $X$5 0.24557 0.51958 0.55146 0.43817
    $X$6 0.95095 0.40619 0.24412 0.50126 0.99107
  • Table 2   The accuracy of FS-SAE models for the mission
    Model Training set accuracy (%) Testing set accuracy (%)
    FS-SAE 95 82
    Sparsed-SAE 84 73