Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.569
Because artificial neural networks discard the traditional modeling methods, it can extract domain knowledge from a large number of discrete experimental data via study and training, and express these knowledge as network connection weights, so as to establish the corresponding relation model. In this paper, based on neural network BP algorithm, we built a relation model that shows how various process parameters affect the magnesium output rate in Pidgeon magnesium reduction process. This laid a foundation for process parameters optimization.
Neural Network, Process Parameter, Optimization
H. Yuan, J. Zhou and T. Zhou, "The Use of Neural Network BP Algorithm in Magnesium Smelting Process Parameter Optimization," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 600-602.