The Model and Algorithm for the Target Threat Assessment Based on Elman<sub>-</sub>AdaBoost Strong Predictor

WANG Gai-ge; GUO Li-hong; DUAN Hong; LIU Luo; WANG He-qi

doi:10.3969/j.issn.0372-2112.2012.05.007

您当前的位置：

首页 >

文章列表页 >

The Model and Algorithm for the Target Threat Assessment Based on Elman_-AdaBoost Strong Predictor

更新时间：2025-07-16

- The Model and Algorithm for the Target Threat Assessment Based on Elman_-AdaBoost Strong Predictor
- Acta Electronica Sinica Vol. 40, Issue 5, Pages: 901-906(2012)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林,长春,130033
  2. 中国科学院研究生院,北京,100039
  3. 东北师范大学计算机科学与信息技术学院,吉林,长春,130117
  4. 中国科学院长春光学精密机械与物理研究所,吉林,长春,130033
  5. 中国科学院研究生院,北京,100039
  6. 东北师范大学计算机科学与信息技术学院,吉林,长春,130117
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2012.05.007
  CLC： TP391.9
- Published：2012
- 稿件说明：
移动端阅览
WANG Gai-ge, GUO Li-hong, DUAN Hong, et al. The Model and Algorithm for the Target Threat Assessment Based on Elman_-AdaBoost Strong Predictor[J]. Acta Electronica Sinica, 2012, 40(5): 901-906.
DOI：

WANG Gai-ge, GUO Li-hong, DUAN Hong, et al. The Model and Algorithm for the Target Threat Assessment Based on Elman_-AdaBoost Strong Predictor[J]. Acta Electronica Sinica, 2012, 40(5): 901-906. DOI： 10.3969/j.issn.0372-2112.2012.05.007.

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

18和弱预测器数目

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

=7和

=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.

Abstract

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 Elman

AdaBoost strong predictor is established;at last

an algorithm is described.The

re 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

18 and

20 respectively for experiment and results 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

=7 and

=6.The model and algorithm have good predictive ability

so it can quickly and accurately complete target threat assessment.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Grey Model and Algorithm for the Selection of Electronic Equipment Test Project

Pattern Classification Neural Network Model Based on Homologue Connectedness

A Wafer-Level Vacuum Packaged MEMS Electric Field Sensor Based on SOI-SOG Bonding

TSB:A Multi-Stage Algorithm for IPv6 Routing Table Lookup

Related Author

KE Hong-fa

CHEN Yong-guang

LIU Bo

YANG Guo-wei

WANG Shou-jue

WEI Cheng-bing

CAO Wen-yi

LIU Jun

Related Institution

School of Electronic Science and Engineering,National University of Defense Technology

Unit 63880

School of Electronic Science and EngineeringNational University of Defense TechnologyChangshaHunan 410073China

Unit 63880LuoyangHenan 471003China

Nanchang Hangkong University, Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition

⁰

The Model and Algorithm for the Target Threat Assessment Based on Elman-AdaBoost Strong Predictor

DOI：10.3969/j.issn.0372-2112.2012.05.007

摘要

Abstract

关键词

Keywords

references

The Model and Algorithm for the Target Threat Assessment Based on Elman_-AdaBoost Strong Predictor