基于Adaboost的作战目标属性判定方法
E92; 传统作战目标属性判定主要采用指挥员现场判断的定性方法,具有一定的主观性,并且由于缺乏较为成熟固定的算法而难以纳入指挥平台中.针对此问题,结合作战目标属性判定关键影响因素分析,提出一种基于自适应提升(adaptive boosting,Adaboost)的作战目标属性判定方法.首先,针对目标有效面积、目标配置区域面积等关键因素,采用单层决策树算法构建弱分类器.然后,利用Adaboost对弱分类器进行加权组合,形成作战目标属性判定的强分类模型.最后,进行了示例分析,并与决策树、支持向量机和人工神经网络3种属性判定方法进行对比仿真实验,证明了所提方法的正确性和优越性....
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| Published in | 系统工程与电子技术 Vol. 44; no. 4; pp. 1256 - 1262 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
陆军工程大学石家庄校区装备指挥与管理系,河北石家庄050003%陆军步兵学院石家庄校区机械化步兵系,河北石家庄050227
01.04.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-506X |
| DOI | 10.12305/j.issn.1001-506X.2022.04.23 |
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| Abstract | E92; 传统作战目标属性判定主要采用指挥员现场判断的定性方法,具有一定的主观性,并且由于缺乏较为成熟固定的算法而难以纳入指挥平台中.针对此问题,结合作战目标属性判定关键影响因素分析,提出一种基于自适应提升(adaptive boosting,Adaboost)的作战目标属性判定方法.首先,针对目标有效面积、目标配置区域面积等关键因素,采用单层决策树算法构建弱分类器.然后,利用Adaboost对弱分类器进行加权组合,形成作战目标属性判定的强分类模型.最后,进行了示例分析,并与决策树、支持向量机和人工神经网络3种属性判定方法进行对比仿真实验,证明了所提方法的正确性和优越性. |
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| AbstractList | E92; 传统作战目标属性判定主要采用指挥员现场判断的定性方法,具有一定的主观性,并且由于缺乏较为成熟固定的算法而难以纳入指挥平台中.针对此问题,结合作战目标属性判定关键影响因素分析,提出一种基于自适应提升(adaptive boosting,Adaboost)的作战目标属性判定方法.首先,针对目标有效面积、目标配置区域面积等关键因素,采用单层决策树算法构建弱分类器.然后,利用Adaboost对弱分类器进行加权组合,形成作战目标属性判定的强分类模型.最后,进行了示例分析,并与决策树、支持向量机和人工神经网络3种属性判定方法进行对比仿真实验,证明了所提方法的正确性和优越性. |
| Author | 赵美 李园 史宪铭 李亚娟 |
| AuthorAffiliation | 陆军工程大学石家庄校区装备指挥与管理系,河北石家庄050003%陆军步兵学院石家庄校区机械化步兵系,河北石家庄050227 |
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| Author_FL | SHI Xianming ZHAO Mei LI Yuan LI Yajuan |
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| Publisher | 陆军工程大学石家庄校区装备指挥与管理系,河北石家庄050003%陆军步兵学院石家庄校区机械化步兵系,河北石家庄050227 |
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