| Citation: | FENG Yuxuan, LIU Shuguang, XIE Wujie, et al. Autonomous capability evaluation of ground-attack UAV based on improved Hopfield neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(4): 835-843. doi: 10.13700/j.bh.1001-5965.2020.0040(in Chinese)
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The ground-attack UAV has been one of the most state-of-the-art unmanned equipments, which requires a high degree of autonomous capability. Autonomous capabilityis a typical operational ability of UAV. In view of the quantitative evaluation of autonomous capability forground-attack UAV, this paper proposes a detailed evaluation index system of autonomous capability from four aspects of observation capability, decision capability, action capability and security capability, and places emphasis on the analysis of airborne equipment parameters. Combined with the model factor library, the weight matrix of Hopfield neural network is designed by singular value decomposition, and based on sparsity, the weight reduction algorithm is introduced to improve the network structure. Finally, the evaluation criterion of autonomy is established to quantify and grade the autonomous capability for ground-attack UAV system. The simulation results show that, compared with traditional Hopfield neural network, the improved algorithm can delete the unimportant connection weights within a certain range, reduce the network complexity, and easily achieve quantitative evaluation of the autonomous capability of UAV system.
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