Target threat assessment using improved SVM

	Target threat assessment using improved SVM
	Li J.; Guo L.-H.
	2014
发表期刊	Guangxue Jingmi Gongcheng/Optics and Precision Engineering
ISSN	ISBN/1004924X
卷号	22 期号:5 页码:1354-1362
摘要	On the basis of the characteristics of target threat assessment in information fusion, the weaknesses of traditional methods for target threat assessment and Support Vector Machine (SVM) were analyzed. By using the Particle Swarm Optimization (PSO) to optimize the penalty parameter c and core function g in the SVM, a new target threat assessment model (PSO_SVM) was established and the PSO_SVM algorithm was achieved based on the model. To satisfy the requirements of PSO_SVM algorithm, data was preprocessed, including quantification and normalization. When cross-validation method was used to find the best parameters, the POD was used for network training. 75 group data were used in simulation experiments, among them 60 group data were train sets and the others were test sets. Experimental results show that the error of the PSO_SVM method is 0, reaching the desired goal, which proves the accuracy and efficiency of the proposed method.
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/181722/44438
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Li J.,Guo L.-H.. Target threat assessment using improved SVM[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2014,22(5):1354-1362.
APA	Li J.,&Guo L.-H..(2014).Target threat assessment using improved SVM.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,22(5),1354-1362.
MLA	Li J.,et al."Target threat assessment using improved SVM".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 22.5(2014):1354-1362.

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