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Prediction of Gas Concentration Based on the Opposite Degree Algorithm

Michael McAleer and Xiaoguang Yue ()

Journal of Reviews on Global Economics, 2017, vol. 6, 154-162

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, based on the opposite degree algorithm. A priori and a posteriori values, opposite degree computation, opposite space, prior matrix, and posterior matrix are 6 basic concepts of the 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. The simulation experiments show that the OD-NC algorithm is feasible and effective in practice.

Keywords: Gas concentration; opposite degree algorithm; data prediction; mining safety; numerical simulations (search for similar items in EconPapers)
Date: 2017
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Related works:
Working Paper: Prediction of Gas Concentration Based on the Opposite Degree Algorithm (2016) Downloads
Working Paper: Prediction of Gas Concentration based on the Opposite Degree Algorithm (2016) Downloads
Working Paper: Prediction of Gas Concentration Based on the Opposite Degree Algorithm (2016) Downloads
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Journal of Reviews on Global Economics is currently edited by Michael McAleer and Chia-Lin Chang

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