Abstract：To assist commanders to make a correct judgment on the threat degree of naval vessel targets, this paper establishes a naval vessel threat assessment model based on attribute reduction and (Back Propagation, BP) neural network. Through attribute reduction, typical military operational factors of naval vessel threat assessment are given to verify the technical feasibility of solving the nonlinear naval vessel threat assessment based on BP neural network, and meanwhile, a multi-objective naval vessel threat assessment indicator system is established, which lays a theoretical foundation for importing a large number of combat data to verify the algorithm of naval vessel threat assessment. The results of naval vessel threat assessment are given by validating data. The experiments show that BP neural network can effectively solve the non-linear problem in battle situation assessment accurately and stably, which is greatly significant to realize intelligent battle threat assessment. Moreover, it also provides a technical approach and theoretical basis for the realization of assistant decision-making technology.
孙宇祥, 周献中, 戴迪. 基于属性约简与 BP 神经网络的舰艇目标威胁评估方法[J]. 指挥与控制学报, 2021, 7(4): 397-402.
SUN Yu-Xiang, ZHOU Xian-Zhong, DAI Di. Threat Assessment Method of Naval Vessel Target Based on Attribute Reduction and BP Neural Network. Journal of Command and Control, 2021, 7(4): 397-402.