TY - JOUR
T1 - 基于DBN效能拟合的舰艇编队作战效能敏感性分析
AU - Li, Bo
AU - Luo, Haoran
AU - Tian, Linyu
AU - Wang, Yuanxun
N1 - Publisher Copyright:
© 2019, Press of Chinese Journal of Aeronautics. All right reserved.
PY - 2019/12/25
Y1 - 2019/12/25
N2 - Aiming at the problem of insufficient data utilization and high requirements for data integrity in the traditional ship formation combat effectiveness analysis analysis method, this paper proposes a performance analysis fitting model based on deep belief network. Start with the most representative sensitivity analysis method-Sobol index method, and then take characteristic learning ability of deep learning, constructing a effectiveness fitting network based on Deep Belief Network(DBN), with network training and parameter optimization combined with unsupervised pre-training and supervised tuning. Finally, the experiments are simulated and analyzed based on the formation of air defense combat. Simulation results verify the applicability and effectiveness of the model.
AB - Aiming at the problem of insufficient data utilization and high requirements for data integrity in the traditional ship formation combat effectiveness analysis analysis method, this paper proposes a performance analysis fitting model based on deep belief network. Start with the most representative sensitivity analysis method-Sobol index method, and then take characteristic learning ability of deep learning, constructing a effectiveness fitting network based on Deep Belief Network(DBN), with network training and parameter optimization combined with unsupervised pre-training and supervised tuning. Finally, the experiments are simulated and analyzed based on the formation of air defense combat. Simulation results verify the applicability and effectiveness of the model.
KW - Deep Belief Network(DBN)
KW - Effectiveness analysis
KW - Effectiveness fitting model
KW - Sensitivity analysis
KW - Ship formation air defense
UR - http://www.scopus.com/inward/record.url?scp=85079602866&partnerID=8YFLogxK
U2 - 10.7527/S1000-6893.2019.23214
DO - 10.7527/S1000-6893.2019.23214
M3 - 文章
AN - SCOPUS:85079602866
SN - 1000-6893
VL - 40
JO - Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica
JF - Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica
IS - 12
M1 - 323214
ER -