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

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

References
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Cite This Article
  • 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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    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
  • College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China

  • Library of Gannan Normal University, Gannan Normal University, Ganzhou, China

  • College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China

  • College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China

  • College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China

  • College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China

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