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Threat assessment of support vector machine optimized by Krill Herd algorithm
Huang, X.; L.-H. Guo; J. Li and Y. Yu
2016
发表期刊Guangxue Jingmi Gongcheng/Optics and Precision Engineering
卷号24期号:6
摘要In order to improve exact recognition ratios for aerial targets, this paper presents a novel algorithm for target recognition based on interval-valued intuitionistic fuzzy sets with grey correlation. Drawbacks of some previously proposed methods are analyzed, and then a novel algorithm is presented. Recognition matrix of an aerial target is established first. Every entry associated with the matrix is an interval-valued intuitionistic fuzzy number, which is composed of interval-valued membership and nonmembership, representing the relation of the target to one category in terms of one characteristic parameter. Then grey correlation theory is used to analyze the recognition matrix to obtain the grey correlation degree of this unknown target to every category. 200 sets of target recognition data are used to compare the proposed algorithm with traditional methods. Experimental results verify that the correct recognition ratio can be up to 99.5% that satisfies the expectations, which shows the proposed algorithm can solve the target recognition problems better. The proposed algorithm can be used to solve the uncertain inference problems, such as target recognition, threat assessment, and decision making.
文章类型期刊
收录类别EI
语种中文
文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/56973
专题中科院长春光机所知识产出
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GB/T 7714
Huang, X.,L.-H. Guo,J. Li and Y. Yu. Threat assessment of support vector machine optimized by Krill Herd algorithm[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2016,24(6).
APA Huang, X.,L.-H. Guo,&J. Li and Y. Yu.(2016).Threat assessment of support vector machine optimized by Krill Herd algorithm.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,24(6).
MLA Huang, X.,et al."Threat assessment of support vector machine optimized by Krill Herd algorithm".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 24.6(2016).
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