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Prediction of gas concentration based on the opposite degree algorithm

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Yue, Xiao-Guang and Gao, Rui and McAleer, Michael (2016) Prediction of gas concentration based on the opposite degree algorithm. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 05, 2016, ] (Unpublished)

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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.


Item Type:Working Paper or Technical Report
Uncontrolled Keywords:Gas concentration, Opposite degree algorithm, Data prediction, Mining safety, Numerical simulations.
Subjects:Sciences > Geology > Mineralogy
Sciences > Statistics > Mathematical optimization
Social sciences > Economics > Econometrics
JEL:C53, C63, L71
Series Name:Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)
Volume:2016
Number:05
ID Code:37245
Deposited On:21 Apr 2016 08:56
Last Modified:21 Apr 2016 08:56

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