基于模型融合和生成网络的有效阵位智能决策方法

Effective Position Intelligent Decision Method Based on Model Fusion and Generative Network

军事智能技术是当前最具活力的前沿领域和未来无人装备发展的必然趋势。针对无人平台在复杂环境下自主决策可靠性和实时性的双重需求和现有基于规则推演的作战仿真技术在动态性和灵活性方面的不足,采用原理分析与实验验证的研究方法,在某型无人平台射击实验数据集的基础上,围绕攻击决策的有效阵位识别环节,将其转换为机器学习领域类别不平衡的二分类问题,综合采用相关性分析、特征工程、模型融合技术构建高实时性和灵活性的有效阵位智能决策模型,并提出基于ICGAN-Stacking不平衡分类架构对少数类样本进行定向扩充,实现数据增强和模型性能提升。实验结果表明:所提方法召回率提升了4.1%、精确度提升了0.4%、F1值提升了1.5%、AUC值达到90.9%,能够满足无人平台执行作战任务实时性和可靠性需求。

Military intelligence technology is currently the most dynamic frontier and the inevitable trend for the development of unmanned equipment in the future.Aiming at the dual requirements of reliability and real-time performance of unmanned platform autonomous decision-making in complex environments and the shortcomings of existing combat simulation technology based on rule reasoning in terms of dynamics and flexibility,a research method of principle analysis and experimental verification is adopted.Based on the shooting experiment dataset of an unmanned platform,the effective position recognition link of attack decision-making is transformed into a binary classification problem with imbalanced categories in the field of machine learning.The effective position intelligent decision-making model with high real-time performance and flexibility is constructed by using correlation analysis,feature engineering,and model fusion technology.Based on the imbalanced classification architecture of ICGANStacking,directional expansion of minority class samples is proposed to achieve data enhancement and model performance improvement.The experimental results show that the recall rate of the proposed method has increased by 4.1%,the accuracy by 0.4%,and the F1 value by 1.5%,and the AUC value reaches 90.9%,which can meet the real-time performance and reliability requirements of the unmanned platform in performing combat tasks.