| 基于支持向量机的飞行安全隐患危险性评价 |
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| 引用本文： | 甘旭升,端木京顺,丛伟,赵录峰.基于支持向量机的飞行安全隐患危险性评价[J].中国安全生产科学技术,2010,6(3):206-210. |
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| 作者姓名： | 甘旭升 端木京顺 丛伟 赵录峰 |
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| 作者单位： | 1. 西京学院,西安,710123
2. 空军工程大学工程学院,西安,710038 |
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| 摘 要： | 提出了基于支持向量机的飞行安全隐患危险性评价方法,建立了支持向量机模型。并以飞行安全隐患危险性评价的基本要素为输入节点,以评价结果作为输出节点,对空军某部的飞行安全状况进行了评价。结果表明:对于飞行安全隐患危险性评价问题,支持向量机方法较传统神经网络方法精度更高,速度更快,实际应用中也更易于实现。
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| 关 键 词： | 支持向量机 飞行安全 危险性评价 神经网络 |
Fatalness assessment of flight safety hidden danger based on support vector machine |
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| GAN Xu-sheng,DUANMU Jingshun,CONG Wei,ZHAO Lu-feng.Fatalness assessment of flight safety hidden danger based on support vector machine[J].Journal of Safety Science and Technology,2010,6(3):206-210. |
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| Authors: | GAN Xu-sheng DUANMU Jingshun CONG Wei ZHAO Lu-feng |
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| Institution: | 1.XiJing College,Xi＇an 710123,China) (2.College of Engineering,Air Force University of Engineering,Shaanxi,Xi＇an 710038,China) |
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| Abstract: | A method for fatalness assessment of flight safety hidden danger,based on support vector machine（SVM）,was proposed.And the corresponding model,which took the basic assessment factors of flight safety hidden danger fatalness as input node and assessment results as output node,was built.Then the safety situation of a regiment of China Air Force was assessed.The results showed that,for fatalness assessment of flight safety hidden danger,SVM has better performance on precision,rapidity and realization in comparison with the traditional neural network. |
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| Keywords: | support vector machine flight safety fatalness assessment neural network |
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