|本期目录/Table of Contents|

[1]陈建宏,陈浩,郑荣凯,等.基于物元分析与PCA的部队汽车分队安全评价模型[J].中国安全生产科学技术,2014,10(7):180-185.[doi:10.11731/j.issn.1673-193x.2014.07.032]
 CHEN Jian-hong,CHEN Hao,ZHENG Rong-kai,et al.Safety assessment model for military vehicle units based on combination of matter element analysis and PCA[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(7):180-185.[doi:10.11731/j.issn.1673-193x.2014.07.032]
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基于物元分析与PCA的部队汽车分队安全评价模型
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
10
期数:
2014年7期
页码:
180-185
栏目:
职业安全卫生管理与技术
出版日期:
2014-07-31

文章信息/Info

Title:
Safety assessment model for military vehicle units based on combination of matter element analysis and PCA
作者:
陈建宏1陈浩12郑荣凯13杨珊1
(1中南大学 资源与安全工程学院,湖南 长沙 410083;2中国人民解放军66295部队,河北 保定 072761; 3中国人民解放军73096部队,江苏 南京 210059)
Author(s):
CHEN Jian-hong1 CHEN Hao12 ZHENG Rong-kai13 YANG Shan1
(1. School of Resources and Safety Engineering, Central South University, Changsha Hunan 410083, China;  2. 66295 unit of People's Liberation Army, Baoding Hebei 072761, China;  3. 73096 unit of People's Liberation Army, Nanjing Jiangsu 210059, China)
关键词:
主成分分析BP神经网络物元分析部队汽车分队安全评价
Keywords:
principal component analysis back propagation neural network matter element analysis military vehicle unit safety assessment
分类号:
X913.4
DOI:
10.11731/j.issn.1673-193x.2014.07.032
文献标志码:
A
摘要:
为改善部队汽车分队的安全状况,建立了基于主成分分析与BP神经网络的部队汽车分队安全评价模型。在利用层次分析法建立分队系统的安全评价指标体系的基础上,将专家作为样本,进行物元分析,两者联合确定指标权重,进而得到相对客观的评价样本。对样本提取主成分,使输入变量降维且相互独立,以提高网络训练和预测效果。结果表明,其预测精度优于不采用主成分分析的BP网络模型,且相对误差在4%以内,模型具有可行性。因此,结合了物元分析与主成分分析的BP网络耦合模型能更加客观、准确地评价和预测被评价对象的实际安全状况。
Abstract:
In order to improve the safety situation of military vehicle units, a safety assessment model based on PCA and BP neural network was proposed. On the basis of the scientific safety assessment index system designed through AHP, the matter element analysis was conducted by regarding the experts as samples. By the way of combining AHP with matter element analysis, the weights of each index were determined. Then the raw samples, which were relatively objective, were obtained by calculating the corresponding weight and score for each index. Through extracting the main ingredients from the raw samples, the input variables were reduced and unrelated, by which the BP neural network can be trained and predict much better. The results showed that its calculation accuracy was higher than that of the BP neural network without using the PCA, and the relative errors between actual output and expected output were all less than 4%,so it can be applied to assessing the safety situation of military vehicle units. The coupling model of BP neural network combined with principal component analysis and PCA can be more objective and accurate in the aspect of assessing the actual safety situation of evaluation objects.

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备注/Memo

备注/Memo:
国家自然科学基金项目(51374242)
更新日期/Last Update: 2014-07-30