Changchun Institute of Optics,Fine Mechanics and Physics,CAS
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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基于改进支持向量机的目标威胁估计.pdf(1503KB) | 开放获取 | CC BY-ND | 浏览 下载 |
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