Journal of System Simulation
Abstract
Abstract: The modeling of blue army equipment is an indispensable part of adversarial simulation environment construction. Aiming at the limited available parameters of "information-poor" and "small sample" characteristics to the blue system, a deep network-based method is proposed to generate the parameters of blue army equipment model. By injecting the information into the simulation model of the blue army equipment, the simulation data is generated and trained in the deep neural network. The obtained network has a certain generalization ability to the unknown parameters prediction of the same type of equipment and can be used directly in prediction or be the source model for migration learning. The application is verified by the modeling simulation of a certain type of interceptor of blue army in which two kinds of networks, multilayer perceptron and recurrent neural network, are used to learn and predict the proportional guidance coefficients, and the results are good.
Recommended Citation
Zhang, Boyuan; Gong, Guanghong; Wang, Ze; and Li, Ni
(2022)
"Research on Parameter Construction Method of Blue Army Equipment Model Based on a Deep Network,"
Journal of System Simulation: Vol. 34:
Iss.
12, Article 12.
DOI: 10.16182/j.issn1004731x.joss.22-FZ0915
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss12/12
First Page
2629
Revised Date
2022-10-15
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.22-FZ0915
Last Page
2638
CLC
TP391.9
Recommended Citation
Boyuan Zhang, Guanghong Gong, Ze Wang, Ni Li. Research on Parameter Construction Method of Blue Army Equipment Model Based on a Deep Network[J]. Journal of System Simulation, 2022, 34(12): 2629-2638.
DOI
10.16182/j.issn1004731x.joss.22-FZ0915
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