Journal of System Simulation
Abstract
Abstract: To measure and estimate the uncertainty of the battlefield situation is of great significance for the commanders to plan the reconnaissance mission and reduce the risk of decision-making. Based on Shannon's information theory, firstly, methods and a model on measurement of situation change rate are proposed. Secondly, a scene with two-dimensional grid elements maneuvering is established, based on deep learning, the prediction method for maneuvering trend is explored. It is proved that cross entropy is equivalent to situation change rate. Finally, with the increase of the objective uncertainty, situation change rate and the accuracy of the forecast is analyzed. It is deduced that there is an upper limit on the prediction accuracy based on the learning model, and the upper limit is inversely proportional to the situation change rate.
Recommended Citation
Tao, Jiuyang; Lin, Wu; Chi, Wang; Chu, Junda; Ying, Liao; and Feng, Zhu
(2019)
"A Model for Battlefield Situation Change Rate Prediction Based on Deep Learning,"
Journal of System Simulation: Vol. 30:
Iss.
3, Article 3.
DOI: 10.16182/j.issn1004731x.joss.201803003
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol30/iss3/3
First Page
785
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.201803003
Last Page
792
CLC
TP391.9
Recommended Citation
Tao Jiuyang, Wu Lin, Wang Chi, Chu Junda, Liao Ying, Zhu Feng. A Model for Battlefield Situation Change Rate Prediction Based on Deep Learning[J]. Journal of System Simulation, 2018, 30(3): 785-792.
DOI
10.16182/j.issn1004731x.joss.201803003
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