Template-type: ReDIF-Paper 1.0
Author-Name: Xiao-Guang Yue
Author-Workplace-Name: School of Civil Engineering Wuhan University, China
Author-Name: Rui Gao
Author-Workplace-Name:
 School of Civil Engineering Wuhan University, China
Author-Name: Michael McAleer
Author-Workplace-Name: Department of Quantitative Finance National Tsing Hua University, Taiwan
Title: Prediction of Gas Concentration Based on the Opposite Degree Algorithm
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.
Classification-JEL: C53, C63, L71
Keywords: Gas concentration, Opposite degree algorithm, Data prediction, Mining safety, Numerical simulations.
Length: 36 pages 
Creation-Date: 2016-04
Number: 2016-05
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1605.txt
File-URL: https://eprints.ucm.es/id/eprint/37245/1/1605.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1605