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Prediction of Gas Concentration based on the Opposite Degree Algorithm

Listed author(s):
  • Xiao-Guang Yue

    (Wuhan University, China)

  • Rui Gao

    (Wuhan University, China)

  • Michael McAleer

    (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, the Netherlands; Complutense University of Madrid, Spain)

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.

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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 16-027/III.

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Date of creation: 19 Apr 2016
Handle: RePEc:tin:wpaper:20160027
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