Application of Artificial Neural Networks for Airline Number of Passenger Estimation in Time Series State

Research Article

Application of Artificial Neural Networks for Airline Number of Passenger Estimation in Time Series State

M. Zandieh, A. Azadeh, B. Hadadi and M. Saberi

This study presents an integrated Artificial Neural Networks (ANN) to estimate and predict airline number of passenger in Iran. All type of ANN-Multi Layer Perceptron (MLP) is examined to this estimation. The ANN models are implemented on MATLAB software. Auto-Correlation Function (ACF) is utilized to define input variables. Finally, the best type of ANN-MLP is determined with Data Envelopment Analysis (DEA). Kruskal-Wallis test is used for asses the impact of raw data, preprocessed data and post process method on ANN performance. Monthly airline number of passenger of Iran airline from 1993 to 2005 is considered as the case of this study.

PDF Fulltext XML References Citation

How to cite this article

M. Zandieh, A. Azadeh, B. Hadadi and M. Saberi, 2009. Application of Artificial Neural Networks for Airline Number of Passenger Estimation in Time Series State. Journal of Applied Sciences, 9: 1001-1013.

DOI: 10.3923/jas.2009.1001.1013

URL: https://scialert.net/abstract/?doi=jas.2009.1001.1013

Journal of Applied Sciences

Article Trend

Total views 304

Search

Authors

M. Zandieh

A. Azadeh

B. Hadadi

M. Saberi

Keywords

Research Article

Application of Artificial Neural Networks for Airline Number of Passenger Estimation in Time Series State

How to cite this article

Related Articles

Leave a Comment