Journal of System Simulation
Abstract
Abstract: Aiming at the two difficulties in characteristic index digging of combat system of systems (CSoS), namely operation data generation and digging method selection, this paper proposes a new digging method, that is, using the simulation testbed to generate operation data, then adopting the machine learning to dig characteristic index. Two methods of characteristic index digging based on machine learning are researched: (1) the method based on network convergence, divides the communities for fundamental indexes based on their relationship, and obtains the characteristic indexes by principal component analysis (PCA); this method is applied to dig the characteristic indexes of air defense ability. (2) the method based on ensemble learning, generates test data by bagging, trains model by CART decision trees, and obtains the characteristic indexes by PCA; this method is applied to dig the characteristic indexes of air defense breakthrough ability.
Recommended Citation
Yang, Yongli; Hu, Xiaofeng; Ming, Rong; Yin, Xiaojing; and Wang, Wenxiang
(2019)
"Characteristic Index Digging of Combat SoS Capability Based on Machine Learning,"
Journal of System Simulation: Vol. 31:
Iss.
6, Article 3.
DOI: 10.16182/j.issn1004731x.joss.19-0238
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol31/iss6/3
First Page
1048
Revised Date
2019-06-10
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-0238
Last Page
1054
CLC
TP391.9
Recommended Citation
Yang Yongli, Hu Xiaofeng, Rong Ming, Yin Xiaojing, Wang Wenxiang. Characteristic Index Digging of Combat SoS Capability Based on Machine Learning[J]. Journal of System Simulation, 2019, 31(6): 1048-1054.
DOI
10.16182/j.issn1004731x.joss.19-0238
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