基于机器学习的作战体系能力特征指标挖掘

Characteristic Index Digging of Combat SoS Capability Based on Machine Learning

针对当前作战体系能力特征指标挖掘存在的两个困难:作战数据生成和挖掘方法选择,提出"先采用仿真试验床生成作战数据,再利用机器学习挖掘特征指标"的方法。研究了2种基于机器学习的特征指标挖掘方法:基于网络聚合的方法,依据基础指标的相关性进行社团划分,利用主成分分析法得到特征指标,应用该方法挖掘防空能力的特征指标;基于集成学习的方法,利用装袋法生成测试数据集和CART决策树训练模型,利用主成分分析法得到特征指标,应用该方法挖掘防突入能力的特征指标。

Aiming at the two difficulties in characteristic index digging of combat system of systems(CSoS),namely operation data generation and digging method selection, this paper proposes a new digging method,that is, using the simulation testbed to generate operation data, then adopting the machine learning to dig characteristic index. Two methods of characteristic index digging based on machine learning are researched:(1)the method based on network convergence, divides the communities for fundamental indexes based on their relationship, and obtains the characteristic indexes by principal component analysis(PCA);this method is applied to dig the characteristic indexes of air defense ability.(2) the method based on ensemble learning,generates test data by bagging, trains model by CART decision trees, and obtains the characteristic indexes by PCA;this method is applied to dig the characteristic indexes of air defense breakthrough ability.