基于Elman_AdaBoost强预测器的目标威胁评估模型及算法

摘要

目标威胁评估是协同目标攻击中的关键问题.为提高空战目标威胁评估的准确性和实用性,建立了Elman_ AdaBoost强预测器目标威胁评估模型及算法.首先,介绍了Elman_ AdaBoost强预测器；其次,建立了Elman_AdaBoost强预测器目标威胁评估模型；最后,提出了基于Elman_ AdaBoost强预测器目标威胁评估模型的算法.采集75组 数据用于实验,其中60组作为训练集,15组作为测试集.分别选择Elman网络隐层节点数L=7,11,14,18和弱预测器数目K=6,10,16,20进行实验,结果表明,Elman_AdaBoost强预测器算法预测误差远小于弱预测器且在L=7和K=6时误差达到最小.Elman_ AdaBoost强预测器目标威胁评估模型和算法具有很好的预测能力,可以快速、准确地完成作战目标威胁评估.%Target threat assessment is the key issue in the collaborative multi-target attack.To improve the accuracy and usefulness of target threat assessment in the aerial combat,a target threat assessment model and algorithm based on Elman.AdaBoost strong predictor is proposed.Firstly,Elman.AdaBoost strong predictor is introduced;secondly,a target threat assessment model based on Oman.AdaBoost strong predictor is established;at last,an algorithm is described.There are 75 data sets culled for the simulation experiments,in which 60 sets are considered as training set,and the other 15 are testing sets.The number of hidden layer nodes of Oman network and weak predictors is selected L = 7,11,14,18 and K = 6,10,16,20 respectively for experiment and results show that,the prediction error for Oman.AdaBoost strong predictor algorithm is much smaller man the weak predictor and the error reaches the minimum when L = 7 and K = 6.The model and algorithm have good predictive ability,so it can quickly and accurately complete target threat assessment.