基于多标签学习的探索性仿真实验因素筛选方法

Factors screening method of explorative simulationexperiment based on multi-label learning

探索性仿真实验是一种认识、研究战争的重要手段,但往往面临想定样本空间复杂程度高,空间维度爆炸等问题。针对上述问题,提出一种定性定量相结合的基于多标签学习的实验因素筛选方法。首先,基于定性分析设计并实施仿真预实验,采集处理实验数据,解决机器学习样本数据缺失问题;随后,引入输入控制层搭建深度神经网络,引入稀疏正则化,在多标签模型训练过程中实现特征选择;然后,回归定性分析,补充完善实验因素;最后,以某作战样式下立体投送行动仿真推演为背景进行实验验证。实验结果表明,筛选的实验因素与作战行动现实情况吻合。

Explorative simulation experiment is an important mean to understand and study warfare,but it often faces problems such as high complexity of scenario sample space and explosion of space dimensions.To solve all above problems,a qualitative and quantitative method for screening experimental factors based on multi-label learning(MLL)is proposed.First,based on qualitative analysis,a pre-simulation experiment is designed and implemented,experimental data are collected and processed,and the problem of missing machine learning sample data is solved.Then,the input control layer is introduced to build a deep neural network,and sparse regularization is introduced to achieve feature selection during multi-label model training.Then,regression qualitative analysis is used to supplement and improve the experimental factors.Finally,the simulation of stereoscopic projection action is used for experimental verification.The experimental result shows that the method is feasible and valuable in military.