Title :
Application of BP-ANN in Forecasting the Equivalent Radon Exhalation Rate of Uranium Ore-Rock in the Course of Mine Ventilation
Author :
Ye, Yongjun ; Ding, Dexin ; Li, Xiangyang ; Zhou, Xinhuo ; Tan, Yan
Author_Institution :
Key Discipline Lab. for Nat. Defence for Biotechnol. in Uranium Min. & Hydrometallurgy, Univ. of South China, Hengyang, China
Abstract :
The process of radon exhalation of uranium ore-rock in the course of mine ventilation is a complex nonlinear dynamic system. In view of the ability of BP-ANN in dealing with the complex nonlinear function, taking the ventilation air quantity and wind pressure as the input vectors of the BP-ANN forecasting model, and the equivalent radon exhalation rate as the input vector, the paper established a predicting model of the equivalent radon exhalation rate of uranium ore-rock in the course of mine ventilation based on BP-ANN. The forecasting results are compared with that of the nonlinear regressive forecasting mode, which shows that the forecasting model of BPANN has higher predicting precission. It is feasible to forecast the equivalent radon exhalation rate of uranium ore-rock by the method.
Keywords :
backpropagation; mining; neural nets; nonlinear dynamical systems; radon; regression analysis; rocks; uranium; ventilation; BP-ANN forecasting model; Rn; U; backpropagation; complex nonlinear dynamic system; complex nonlinear function; equivalent radon exhalation rate forecasting; mine ventilation course; nonlinear regressive forecasting mode; prediction model; uranium ore-rock; ventilation air quantity; Artificial neural networks; Biotechnology; Brain modeling; Costs; Laboratories; Nonlinear dynamical systems; Nonlinear systems; Predictive models; Ventilation; Wind forecasting;
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
DOI :
10.1109/CISE.2009.5366769
