基于BP神经网络的航空运输量短期预测模型  

Model for Forecasting Air Transportation Based on Back Propagation Neural Network

作　　者：吴璇[1] 

机构地区：[1]天津大学管理学院,天津300010

出　　处：《西安电子科技大学学报（社会科学版）》2007年第3期67-70,共4页Journal of Xidian University：Social Science Edition

摘　　要：随着我国经济的发展,航空运输业也空前繁荣发展,航空运量受多方面的影响。本文运用误差反传神经网络,综合时间因素、实际进出空港的实际架次与飞机类型、进出空港旅客人数和货物量以及空港气象条件等,探索航空运量的短期预测模型。With development of economics in China, air transportation industry becomes increasingly prosperous. However, it is restricted to many factors. The paper constructs a model for predicting short-term air transportation capability based on back propagation neural network, and consideration of time, type of airplane, times of taking off or landing, quantity of cargo and climate conditions etc.

关 键 词：航空 运量 人工神经网络 预测模型 

分 类 号：F56[经济管理—产业经济]