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.
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This paper is supported by the Social Science Foundation (#HB12GL073) in Hebei Province and the Science Foundation of Educational Department (#GH121003).
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Liu, S., Tian, L. & Huang, Y. A comparative study on prediction of throughput in coal ports among three models. Int. J. Mach. Learn. & Cyber. 5, 125–133 (2014). https://doi.org/10.1007/s13042-013-0201-5
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DOI: https://doi.org/10.1007/s13042-013-0201-5