Journal of System Simulation
Abstract
Abstract: Aiming at the problem for the research on the simulation and modeling of the space information network (SIN) with high complexity, a SIN modeling approach based on simulation data is proposed. On this basis, a deep predictive auto-encoder network for SIN performance indicators and a hybrid forecasting analysis model with random forest regression are designed. Due to the characteristics of high dimensionality and wide distribution of SIN performance indicators, with using the deep learning auto-encoder network, the construction of SIN performance index is realized. Combined with the random forest regression model, the relationship model between the network design parameters of SIN and the performance indicators is built. The case study shows that the hybrid model can not only directly predict the SIN performance indicators, but also can analyze the sensitivity of the network design parameters.
Recommended Citation
Xing, Yang; Jing, Wu; Zhou, Jianguo; Hao, Jiang; and Jie, Zhu
(2019)
"Simulation Data-Driven Modeling Approach for Space Information Network,"
Journal of System Simulation: Vol. 30:
Iss.
11, Article 33.
DOI: 10.16182/j.issn1004731x.joss.201811033
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol30/iss11/33
First Page
4323
Revised Date
2018-06-08
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.201811033
Last Page
4334
CLC
TP391.9
Recommended Citation
Yang Xing, Wu Jing, Zhou Jianguo, Jiang Hao, Zhu Jie. Simulation Data-Driven Modeling Approach for Space Information Network[J]. Journal of System Simulation, 2018, 30(11): 4323-4334.
DOI
10.16182/j.issn1004731x.joss.201811033
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