Forecasting flight time based on BP neural network

摘要：

An accurate estimated flight time is essential to modern 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. Simulation results show that the method has rapid convergence and good scalability to accurately forecast flight time.

关键词： Flight Time BP Neural Network Forecast Air Traffic Management

作者: Ruiying Wen Hongyong Wang

作者单位: Air Traffic Management College, Civil Aviation University of China, Tianjin 300300, China Air Traffic Management Research Base, Civil Aviation University of China, Tianjin 300300, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 4232-4236

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）