Forecasting flight time based on BP neural network

被引：0
|
作者：
Wen, Ruiying [1 ]
Wang, Hongyong [2 ]
机构：
[1] Civil Aviat Univ China, Air Traff Management Coll, Tianjin 300300, Peoples R China
[2] Civil Aviat Univ China, Air Traff Management Res Base, Tianjin 300300, Peoples R China
来源：
2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5 | 2010年
关键词：
Flight Time; BP Neural Network; Forecast; Air Traffic Management;
D O I：
10.1109/CCDC.2010.5498389
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An accurate estimated flight time is essential to modem air traffic management systems. Because the forecast is associated with many factors and needs large numbers of statistical calculation, the traditional methods used to forecast flight time are limited and inadequate. In this article, a back propagation neural network model is presented for forecasting the flight time. Firstly, the main factors impacted on flight time were analyzed and the air traffic control and weather condition factors are input to the model as the key factors. Then the optimal number of hidden nodes was obtained by Bayesian information criterion for speeding up the convergence of BP networks.
引用
收藏
页码：4232 / +
页数：2
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