Advanced Search
MyIDEAS: Login to save this article or follow this journal

Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model

Contents:

Author Info

  • Guo, Qiang
  • Luo, Chang-shou
  • Wei, Qing-feng
Registered author(s):

    Abstract

    Considering the complexity of vegetables price forecast, the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm by using the characteristics of genetic algorithm and neural work. Taking mushrooms as an example, the parameters of the model are analyzed through experiment. In the end, the results of genetic algorithm and BP neural network are compared. The results show that the absolute error of prediction data is in the scale of 10%; in the scope that the absolute error in the prediction data is in the scope of 20% and 15%. The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model, especially the absolute error of prediction data is within the scope of 20%. The accuracy of genetic algorithm based on neural network is obviously good than BP neural network model, which represents the favorable generalization capability of the model.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://purl.umn.edu/117430
    Download Restriction: no

    Bibliographic Info

    Article provided by USA-China Science and Culture Media Corporation in its journal Asian Agricultural Research.

    Volume (Year): 03 (2011)
    Issue (Month): 05 (May)
    Pages:

    as in new window
    Handle: RePEc:ags:asagre:117430

    Contact details of provider:

    Related research

    Keywords: Genetic algorithm; Neural network; Vegetables price; Prediction; China; Agribusiness;

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:ags:asagre:117430. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search).

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 profile, as there may be some citations waiting for confirmation.

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