Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm

Mengshan Li; Genqin Sun; Huaijin Zhang; Keming Su; Bingsheng Chen; Yan Wu

doi:doi:10.11648/j.pamj.20170606.11

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Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm

Mengshan Li, Genqin Sun, Huaijin Zhang, Keming Su, Bingsheng Chen, Yan Wu

Published in Pure and Applied Mathematics Journal (Volume 6, Issue 6)

Received: 18 September 2017 Accepted: 11 October 2017 Published: 14 November 2017

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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.

Published in	Pure and Applied Mathematics Journal (Volume 6, Issue 6)
DOI	10.11648/j.pamj.20170606.11
Page(s)	154-159
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Petroleum Price, Prediction Model, Particle Swarm Optimization, Neural Network

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APA Style

Mengshan Li, Genqin Sun, Huaijin Zhang, Keming Su, Bingsheng Chen, et al. (2017). Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm. Pure and Applied Mathematics Journal, 6(6), 154-159. https://doi.org/10.11648/j.pamj.20170606.11

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ACS Style

Mengshan Li; Genqin Sun; Huaijin Zhang; Keming Su; Bingsheng Chen, et al. Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm. Pure Appl. Math. J. 2017, 6(6), 154-159. doi: 10.11648/j.pamj.20170606.11

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AMA Style

Mengshan Li, Genqin Sun, Huaijin Zhang, Keming Su, Bingsheng Chen, et al. Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm. Pure Appl Math J. 2017;6(6):154-159. doi: 10.11648/j.pamj.20170606.11

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@article{10.11648/j.pamj.20170606.11,
  author = {Mengshan Li and Genqin Sun and Huaijin Zhang and Keming Su and Bingsheng Chen and Yan Wu},
  title = {Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm},
  journal = {Pure and Applied Mathematics Journal},
  volume = {6},
  number = {6},
  pages = {154-159},
  doi = {10.11648/j.pamj.20170606.11},
  url = {https://doi.org/10.11648/j.pamj.20170606.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pamj.20170606.11},
  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.},
 year = {2017}
}

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TY  - JOUR
T1  - Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm
AU  - Mengshan Li
AU  - Genqin Sun
AU  - Huaijin Zhang
AU  - Keming Su
AU  - Bingsheng Chen
AU  - Yan Wu
Y1  - 2017/11/14
PY  - 2017
N1  - https://doi.org/10.11648/j.pamj.20170606.11
DO  - 10.11648/j.pamj.20170606.11
T2  - Pure and Applied Mathematics Journal
JF  - Pure and Applied Mathematics Journal
JO  - Pure and Applied Mathematics Journal
SP  - 154
EP  - 159
PB  - Science Publishing Group
SN  - 2326-9812
UR  - https://doi.org/10.11648/j.pamj.20170606.11
AB  - 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.
VL  - 6
IS  - 6
ER  -

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Author Information

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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Plain Text BibTeX RIS

APA Style

Mengshan Li, Genqin Sun, Huaijin Zhang, Keming Su, Bingsheng Chen, et al. (2017). Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm. Pure and Applied Mathematics Journal, 6(6), 154-159. https://doi.org/10.11648/j.pamj.20170606.11

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ACS Style

Mengshan Li; Genqin Sun; Huaijin Zhang; Keming Su; Bingsheng Chen, et al. Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm. Pure Appl. Math. J. 2017, 6(6), 154-159. doi: 10.11648/j.pamj.20170606.11

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AMA Style

Mengshan Li, Genqin Sun, Huaijin Zhang, Keming Su, Bingsheng Chen, et al. Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm. Pure Appl Math J. 2017;6(6):154-159. doi: 10.11648/j.pamj.20170606.11

Copy | Download

@article{10.11648/j.pamj.20170606.11,
  author = {Mengshan Li and Genqin Sun and Huaijin Zhang and Keming Su and Bingsheng Chen and Yan Wu},
  title = {Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm},
  journal = {Pure and Applied Mathematics Journal},
  volume = {6},
  number = {6},
  pages = {154-159},
  doi = {10.11648/j.pamj.20170606.11},
  url = {https://doi.org/10.11648/j.pamj.20170606.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pamj.20170606.11},
  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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm
AU  - Mengshan Li
AU  - Genqin Sun
AU  - Huaijin Zhang
AU  - Keming Su
AU  - Bingsheng Chen
AU  - Yan Wu
Y1  - 2017/11/14
PY  - 2017
N1  - https://doi.org/10.11648/j.pamj.20170606.11
DO  - 10.11648/j.pamj.20170606.11
T2  - Pure and Applied Mathematics Journal
JF  - Pure and Applied Mathematics Journal
JO  - Pure and Applied Mathematics Journal
SP  - 154
EP  - 159
PB  - Science Publishing Group
SN  - 2326-9812
UR  - https://doi.org/10.11648/j.pamj.20170606.11
AB  - 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.
VL  - 6
IS  - 6
ER  -

Copy | Download