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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2014

01.02.2014 | Original Article

A comparative study on prediction of throughput in coal ports among three models

verfasst von: Shuang Liu, Lixia Tian, Yuansheng Huang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2014

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Abstract

Three forecasting models, i.e., the least squares support vector machine (LSSVM), the neural network with back-propagation algorithm (BP), and a hybrid approach called APSO-LSSVM, are presented in this paper to predict the throughput of coal ports. A comparative study on the prediction accuracy among the three models is conducted. The purpose of this comparative study is to provide some useful guidelines for selecting a more accurate model to predict the throughput. The comparative results experimentally show that, in comparison with LSSVM and BP, the APSO-LSSVM has the more accurate accuracy and the better generalization performance regarding the indexes average error, mean absolute error and mean squared error.
Vorheriger Artikel A novel method for head pose estimation based on the “Vitruvian Man”
Nächster Artikel Surface roughness prediction in end milling process using intelligent systems

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Metadaten
Titel
A comparative study on prediction of throughput in coal ports among three models
verfasst von
Shuang Liu
Lixia Tian
Yuansheng Huang
Publikationsdatum
01.02.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-013-0201-5

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