RT Report
T1 Prediction of gas concentration based on the opposite degree algorithm
A1 Yue, Xiao-Guang
A1 Gao, Rui
A1 McAleer, Michael
AB 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.
YR 2016
FD 2016
LK https://hdl.handle.net/20.500.14352/27565
UL https://hdl.handle.net/20.500.14352/27565
LA eng
DS Docta Complutense
RD 6 abr 2025