空袭主攻方向的卷积神经网络判断模型

doi:10.3969/j.issn.1009-086x.2018.05.02

摘要/Abstract

摘要： 针对传统空袭主攻方向判断方法对评价指标体系准确性、完备性要求高,对于信息模糊、缺失、矛盾等复杂条件适应性不强的问题,将卷积神经网络引入空袭主攻方向的判断模型,利用其较强的非线性建模能力,对信息模糊、含噪等条件适应强的特点,通过大样本集的训练,获得隐含其中的人的经验、知识等直觉思维。实验结果表明,建立的评判模型具有较强的容错能力和良好的鲁棒性。

关键词: 防空作战, 空袭主攻方向, 深度学习, 卷积神经网络, 模糊综合评判, 辅助决策

Abstract: Aiming at the problems that the traditional judgment methods of the main offensive direction of air attack have higher accuracy and completeness requirements on the evaluation index system, and their adaptability to some complex conditions, such as fuzzy information, missing and contradictory information is not strong, the convolution neural network is introduced into the main offensive direction judgment model. With its strong ability of nonlinear modeling and its strong adaptability to fuzzy and noisy information, the implied intuitive thinking of the person, such as experience, knowledge, and so on, is obtained through the study of large sample set. The experiments show that the new evaluation model has strong fault tolerance and good robustness.

Key words: air defense operation, main offensive direction, deep learning, convolutional neural network, fuzzy comprehensive evaluation, decision support

中图分类号:

E919

马新星, 滕克难, 侯学隆. 空袭主攻方向的卷积神经网络判断模型[J]. 现代防御技术, 2018, 46(5): 6-12.

MA Xin-xing, TENG Ke-nan, HOU Xue-long. Forecast Model of Main Air Attack Direction Based on CNN[J]. Modern Defense Technology, 2018, 46(5): 6-12.

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