Los Angeles, CA
March 31, 2009 to April 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
Huiling Yuan, Tianrui Zhou, Jie Zhou, "The Use of Neural Network BP Algorithm in Magnesium Smelting Process Parameter Optimization", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 600-602, doi:10.1109/CSIE.2009.569