A hybrid Model of Neural Network and Grey Theory for Air Traffic Passenger Volume Forecasting

被引：0

作者：

Zhang Yan ^{[1
]}

Zhang Jun ^{[1
]}

机构：

[1] Beijing Univ Aeronaut & Astronaut, Beihang Univ, Sch Elect & Informat Engn, Beijing 100083, Peoples R China

来源：

ADVANCED MEASUREMENT AND TEST, PARTS 1 AND 2 | 2010年 / 439-440卷

关键词：

Grey Theory; Radical Basis Function; Neural Network; Traffic Forecasting; APPROXIMATION;

D O I：

10.4028/www.scientific.net/KEM.439-440.818

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Chinese air traffic passenger volumes have experienced phenomenal growth during the past years. The air traffic volume prediction plays a key role in air traffic flow management system. This paper develops a hybrid model of Neural and Grey Theory for air traffic passenger volume forecasting. The Grey theory is adopted to fit the air traffic data patterns and make the data a higher regularity, and Radical basis function is combined to raise the forecasting accuracy. The model is tested with the Chinese civil aviation passenger volume data from 1998 to 2007 and the result shows that the model is feasible for practical implementations.

引用

页码：818 / 822

页数：5

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