Prediction of Gas Concentration Based on the Opposite Degree Algorithm

Xiaoguang Yue, Rui Gao and Michael McAleer
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Rui Gao: School of Civil Engineering Wuhan University, China

No 2016-05, Documentos de Trabajo del ICAE from Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico

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.

Keywords: Gas concentration; Opposite degree algorithm; Data prediction; Mining safety; Numerical simulations. (search for similar items in EconPapers)
JEL-codes: C53 C63 L71 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2016-04
New Economics Papers: this item is included in nep-cmp
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Related works:
Journal Article: Prediction of Gas Concentration Based on the Opposite Degree Algorithm (2017)
Working Paper: Prediction of Gas Concentration Based on the Opposite Degree Algorithm (2016)
Working Paper: Prediction of Gas Concentration based on the Opposite Degree Algorithm (2016)
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