IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/80109.html
   My bibliography  Save this paper

Prediction of Gas Concentration Based on the Opposite Degree Algorithm

Author

Listed:
  • Yue, X-G.
  • Gao, R.
  • McAleer, M.J.

Abstract

In order to study the dynamic changes in gas concentration, to reduce gas hazards, and to protect and improve mining safety, a new method is proposed to predict gas concentration. The method is based on the opposite degree algorithm. Priori and posteriori values, opposite degree computation, opposite space, prior matrix, and posterior matrix are 6 basic concepts of opposite degree algorithm. Several opposite degree numerical formulae to calculate the opposite degrees between gas concentration data and gas concentration data trends can be used to predict empirical results. The opposite degree numerical computation (OD-NC) algorithm has greater accuracy than several common prediction methods, such as RBF (Radial Basis Function) and GRNN (General Regression Neural Network). The prediction mean relative errors of RBF, GRNN and OD-NC are 7.812%, 5.674% and 3.284%, respectively. Simulation experiments shows that the OD-NC algorithm is feasible and effective.

Suggested Citation

  • Yue, X-G. & Gao, R. & McAleer, M.J., 2016. "Prediction of Gas Concentration Based on the Opposite Degree Algorithm," Econometric Institute Research Papers EI2016-22, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:80109
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/80109/EI2016-22.pdf
    Download Restriction: no

    Other versions of this item:

    More about this item

    Keywords

    Gas concentration; opposite degree algorithm; data prediction; mining safety; numerical simulations;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L71 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Hydrocarbon Fuels

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ems:eureir:80109. 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: (RePub). General contact details of provider: http://edirc.repec.org/data/feeurnl.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.