NDLI: Study on coal logistics demand forecast based on PSO-SVR

Content Provider	IEEE Xplore Digital Library
Author	Chen Pei-you Liu Lu
Copyright Year	2013
Description	Author affiliation: Coll. of Grad. Studies, Heilongjiang Inst. of Sci. & Technol., Harbin, China (Liu Lu) \|\| Coll. of Econ. Manage., Heilongjiang Inst. of Sci. & Technol., Harbin, China (Chen Pei-you)
Abstract	The coal logistics demand in this paper is refer to the demand of coal transportation, mainly including: the railway, highway and waterway freight volume of coal. In consideration of the small and the nonlinear history sample, this paper combines the support vector regression machine (support vector regression, SVR) and Particle Swarm Optimization algorithm, (Particle Swarm Optimization, PSO) to propose PSO-SVR coal logistics demand forecasting model which is suitable for the learning of small samples. Taking Coal railway freight volume for example, the paper first select influence factors and coal railway freight volumes from 1995 to 2011 as the learning samples to establish the “influence factors - coal railway freight volume” SVR model and then use the particle swarm algorithm to optimize model parameters, Finally, it forecasts the coal railway freight volume. The results show that the prediction accuracy of PSO-SVR model is superior to the BP neural network model.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	130
Ending Page	133
File Size	485845
Page Count	4
File Format	PDF
e-ISBN	9781467348430
DOI	10.1109/ICSSSM.2013.6602656
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training particle swarm algorithm Coal Predictive models Rail transportation support vector regression machine Mathematical model Logistics coal railway freight volume forecasting
Content Type	Text
Resource Type	Article

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