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。
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.
出处
《中国安全生产科学技术》
CAS
北大核心
2010年第3期206-210,共5页
Journal of Safety Science and Technology
关键词
支持向量机
飞行安全
危险性评价
神经网络
support vector machine
flight safety
fatalness assessment
neural network
