IDEAS home Printed from https://ideas.repec.org/a/ags/asagre/113491.html
   My bibliography  Save this article

A Prediction Model of Peasants’ Income in China Based on BP Neural Network

Author

Listed:
  • Guo, Qing-chun
  • He, Zhen-fang
  • Li, Li
  • Kong, Ling-jun
  • Zhang, Xiao-yong
  • Kou, Li-qun

Abstract

According to the related data affecting the peasants’ income in China in the years 1978-2008, a total of 13 indices are selected, such as agricultural population, output value of primary industry, and rural employees. According to standardized method and BP neural network method, the peasants’ income and the artificial neural network model are established and analyzed. Results show that the simulation value agrees well with the real value; the neural network model with improved BP algorithm has high prediction accuracy, rapid convergence rate and good generalization ability. Finally, suggestions are put forward to increase the peasants’ income, such as promoting the process of urbanization, developing small and medium-sized enterprises in rural areas, encouraging intensive operation, and strengthening the rural infrastructure and agricultural science and technology input.

Suggested Citation

  • Guo, Qing-chun & He, Zhen-fang & Li, Li & Kong, Ling-jun & Zhang, Xiao-yong & Kou, Li-qun, 2011. "A Prediction Model of Peasants’ Income in China Based on BP Neural Network," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 3(04), pages 1-4, April.
    • Handle: RePEc:ags:asagre:113491
    DOI: 10.22004/ag.econ.113491
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/113491/files/4-21.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.113491?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. Shaddel, Mehdi & Javan, Dawood Seyed & Baghernia, Parisa, 2016. "Estimation of hourly global solar irradiation on tilted absorbers from horizontal one using Artificial Neural Network for case study of Mashhad," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 59-67.

    More about this item

    Keywords

    Agribusiness;

    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:ags:asagre:113491. 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: AgEcon Search (email available below). General contact details of provider: .

    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.