摘要
为提高航空业安全水平,预测飞行过程中存在的主要事故风险,本文基于2002年2月至2014年2月的民航事故数据统计,采用SVM和RBF神经网络加权组合模型对民航不安全事件发生原因及飞行阶段事故数量进行了预测。通过对3种典型预测方法的均方根误差比较,证明了此方法的可行性与有效性,结论可为民航安全管理提供科学依据。
This paper predicts the number of civil aviation unsafe event factors and stages with SVM and RBF neural network weighted combination model to improve the level of aviation safety and predict major flight accident risk,which is based on the accident data from Feb.2002 to Feb.2014.Its feasibility and effectiveness are proved by the comparison for the root mean square errors of three typical prediction methods.This can provide a scientific reference for the administration of civil aviation security.
出处
《山东科学》
CAS
2014年第3期61-65,72,共6页
Shandong Science
关键词
不安全事件预测
SVM
RBF神经网络
prediction of unsafe events
support vector machine
RBF neural network
