Estimation of Number of Flight Using Particle Swarm Optimization and Artificial Neural Network

被引：1
|
作者：
Ozmen, Ebru Pekel [1 ]
Pekel, Engin [2 ]
机构：
[1] Istanbul Univ Cerrahpasa, Ind Engn Dept, Istanbul, Turkey
[2] Hitit Univ, Ind Engn Dept, Corum, Turkey
来源：
ADCAIJ-ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL | 2019年 / 8卷 / 03期
关键词：
artificial neural network; airport; particle swarm optimization; estimation; AIR PASSENGER;
D O I：
10.14201/ADCAIJ2019832733
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The number of flight (NF) is one of the key factors for the administration of the airport to evaluate the apron capacity and airline companies to fix the size of the flight. This paper aims to estimate the monthly NF by performing particle swarm optimization (PSO) and artificial neural network (ANN). Performed PSO-ANN algorithm aims to minimize the proposed evaluation criterion in the training stage. PSO-ANN based on the proposed evaluation criterion offers satisfying fitness values with respect to correlation coefficient and mean absolute percentage error in the training and testing stage.
引用
收藏
页码：27 / 33
页数：7
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