IDEAS home Printed from https://ideas.repec.org/a/hin/jjmath/1487746.html
   My bibliography  Save this article

Forecast of Chemical Export Trade Based on PSO-BP Neural Network Model

Author

Listed:
  • Na Li
  • Meng Li
  • Miaochao Chen

Abstract

With the gradual deepening of China’s reform and opening up, the degree of foreign development has been deepened, and its dependence on foreign trade has increased. The “export-oriented†economic development has achieved results. Export trade is introducing advanced technology and equipment, expanding employment opportunities, and increasing government revenue. The export trade is affected by various domestic and international factors and is a complex nonlinear system. Although the traditional linear prediction method has the advantages of intuitiveness, simplicity, and strong interpretability, it is difficult to deal with the prediction problem of dynamic and complex nonlinear systems. The neural network is a nonlinear dynamic system, with strong nonlinear mapping ability, strong robustness, and fault tolerance. It has unique advanced advantages for solving nonlinear problems and is very suitable for solving nonlinear problems.

Suggested Citation

  • Na Li & Meng Li & Miaochao Chen, 2022. "Forecast of Chemical Export Trade Based on PSO-BP Neural Network Model," Journal of Mathematics, Hindawi, vol. 2022, pages 1-10, March.
  • Handle: RePEc:hin:jjmath:1487746
    DOI: 10.1155/2022/1487746
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/jmath/2022/1487746.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/jmath/2022/1487746.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/1487746?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Miao He & Junli Huang & Ruyi Sun, 2023. "Forecast of Advanced Human Capital Gap Based on PSO-BP Neural Network and Coordination Pathway: Example of Beijing–Tianjin–Hebei Region," Sustainability, MDPI, vol. 15(5), pages 1-18, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jjmath:1487746. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.