Airport Flight Departure Delay Model on Improved BN Structure Learning

被引：0

作者：

Cao, Weidong ^{[1
]}

Fang, Xiangnong ^{[2
]}

机构：

[1] Civil Aviat Univ China, Comp Sci & Technol Coll, Tianjin, Peoples R China

[2] Civil Aviat Univ China, Basic Expt Ctr, Tianjin, Peoples R China

来源：

2011 AASRI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRY APPLICATION (AASRI-AIIA 2011), VOL 1 | 2011年

关键词：

Flight Departure Delay; Genetic Algorithm; Simulated Annealing Algorithm; High Score Prior Genetic Simulated Annealing Bayesian Network Structure Learning(HSPGSA);

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An high score prior genetic simulated annealing Bayesian network structure learning algorithm (HSPGSA) by combining genetic algorithm(GA) with simulated annealing algorithm(SAA) is developed. The new algorithm provides not only with strong global search capability of GA, but also with strong local hill climb search capability of SAA. The structure with the highest score is prior selected. In the mean time, structures with lower score are also could be choice. It can avoid efficiently prematurity problem by higher score individual wrong direct growing population. Algorithm is applied to flight departure delays analysis in a large hub airport. Based on the flight data a BN model is created. Experiments show that parameters learning can reflect departure delay.

引用

页码：415 / 418

页数：4