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E-lman_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_Ad-aBoost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 use- fulness 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 Elman_ 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 Elman network and weak predictors is selected L = 7,11,14,18 and K = 6,10,16,20 respectively for experiment and re- suits show that,the prediction error for Elman_ AdaBoost strong predictor algorithm is much smaller than 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 ac- curately complete target threat assessment.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2012年第5期901-906,共6页
Acta Electronica Sinica
基金
激光与物质相互作用国家重点实验室研究基金(No.SKLLIM0902-01)
