IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/918307.html
   My bibliography  Save this article

Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover

Author

Listed:
  • Yueru Ma
  • Lijun Peng

Abstract

The tendency of labor turnover in the Chinese enterprise shows the characteristics of seasonal fluctuations and irregular distribution of various factors, especially the Chinese traditional social and cultural characteristics. In this paper, we present a coupled model for the tendency prediction of labor turnover. In the model, a time series of tendency prediction of labor turnover was expressed as trend item and its random item. Trend item of tendency prediction of labor turnover is predicted using Grey theory. Random item of trend item is calculated by artificial neural network model (ANN). A case study is presented by the data of 24 months in a Chinese matured enterprise. The model uses the advantages of “accumulative generation” of a Grey prediction method, which weakens the original sequence of random disturbance factors and increases the regularity of data. It also takes full advantage of the ANN model approximation performance, which has a capacity to solve economic problems rapidly, describes the nonlinear relationship easily, and avoids the defects of Grey theory.

Suggested Citation

  • Yueru Ma & Lijun Peng, 2014. "Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-6, April.
  • Handle: RePEc:hin:jnlmpe:918307
    DOI: 10.1155/2014/918307
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2014/918307.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2014/918307.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2014/918307?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
    ---><---

    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:jnlmpe:918307. 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.