Citation: | ZHANG Binchao, KOU Yanan, WU Meng, et al. Close-range air combat situation assessment using deep belief network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(7): 1450-1459. doi: 10.13700/j.bh.1001-5965.2016.0956(in Chinese) |
Considering the difficulty in parameter setting, weakness of traditional situation assessment methods in processing and feature extraction of big data, feature of air combat data, applications of deep belief network (DBN) to close-range air combat situation assessment are discussed. A sample library of combat situation was constructed. The data were clustered using density peaks algorithm, and the results were revised by specialists of air combat and traditional functions. Then the model of deep belief network was constructed. According to the standard of test and reconstruction error, the network topology structure and optimal parameters were determined. The model was trained by the data from the sample library. Experimental results show that the model's situation classification accuracy reaches to 92.7%, and its running time meets the application requirements. Analysis of the practical example verified the feasibility of the DBN model.
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