Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm
Pure and Applied Mathematics Journal
Volume 6, Issue 6, December 2017, Pages: 154-159
Received: Sep. 18, 2017; Accepted: Oct. 11, 2017; Published: Nov. 14, 2017
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Authors
Mengshan Li, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
Genqin Sun, Library of Gannan Normal University, Gannan Normal University, Ganzhou, China
Huaijin Zhang, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
Keming Su, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
Bingsheng Chen, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
Yan Wu, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
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Abstract
Petroleum price are affected by some uncertainties and nonlinear factors, how to predict the price effectively is the focus of the present study. In this paper, a 3 layers back propagation artificial neural network model based on particle swarm optimization algorithm combined with chaos theory and self-adaptive weight strategy is developed, the model structure is 7-13-1, and used to predict the petroleum price. By comparing with the other models, it shows that the model proposed in this paper has good prediction performance, the prediction accuracy and correlations are better.
Keywords
Petroleum Price, Prediction Model, Particle Swarm Optimization, Neural Network
To cite this article
Mengshan Li, Genqin Sun, Huaijin Zhang, Keming Su, Bingsheng Chen, Yan Wu, Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm, Pure and Applied Mathematics Journal. Vol. 6, No. 6, 2017, pp. 154-159. doi: 10.11648/j.pamj.20170606.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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