基于套索算法和改进正余弦优化支持向量回归的目标威胁估计  

Target threat estimation based on lasso algorithm and improved sine cosine optimized support vector regression

作　　者：李威[1] 卢盈齐[1] 范成礼[1] LI Weiy;LU Ying-qi;FAN Cheng-li(Air and Missile Defense College,Air Force Engineering University,Xi’an 710000,China)

机构地区：[1]空军工程大学防空反导学院,西安710000

出　　处：《控制与决策》2023年第9期2470-2478,共9页Control and Decision

基　　金：国家自然科学基金项目(62106283,72001214);陕西省自然科学基础研究计划课题项目(2020JQ-484)。

摘　　要：准确估计空中目标的威胁值对于防空作战指挥决策具有重要的参考意义.针对空中目标特征繁杂易造成模型过拟合、正余弦算法易早熟以及陷入局部最优的不足,首先,通过套索算法(least absolute shrinkage and selection operator,LASSO)去除目标的冗余特征;然后,采用佳点集初始化种群、非线性振幅调整因子、随机惯性权重、自适应终点权重以及最优邻域高斯扰动等策略对正余弦算法(sine cosine algorithm,SCA)进行改进;最后,使用改进的正余弦算法对支持向量回归(support vector regression,SVR)模型进行优化,构建基于套索算法和改进正余弦优化支持向量回归的目标威胁估计模型.对比实验结果表明,改进后的正余弦算法加强了全局搜索能力和局部收敛速度,得到的目标威胁估计模型具有较高的准确度和稳定性,能够为防空作战指挥决策提供科学的参考依据.Accurately estimating the threat value of air targets has important reference significance for air defense combat command decision-making.In view of the complex features of aerial targets that easily cause model over-fitting and the sine cosine algorithm is prone to premature and fall into local optimality,this paper uses the least absolute shrinkage and selection operator(LASSO)to remove the redundant features of the target,and then improves the sine cosine algorithm(SCA)with some strategies such as good point set initialization population,nonlinear amplitude adjustment factor,random inertia weight,adaptive end point weight,and uses the improved sine cosine algorithm to optimize the support vector regression(SVR)model,and a target threat estimation model based on lasso algorithm and improved sine cosine optimized support vector regression is constructed.The comparative experimental results show that the improved sine and cosine algorithm enhances the global search ability and local convergence speed,and the obtained target threat estimation model has high accuracy and stability,which can provide a scientific reference for air defense combat command decision-making.

关 键 词：套索算法 正余弦优化 支持向量回归 威胁估计 佳点集 邻域高斯扰动 

分 类 号：E917[军事]