Title :
Research on the prediction model for recovery rate of alloying elements
Author :
Xiaoke Fang ; Jianhui Wang ; Wenle Zhang
Author_Institution :
Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
Abstract :
It is well known that alloying model has great effect on steel quality, and the precision of alloying model relies heavily on the calculation of recovery rate of alloying elements. Aiming at steel quality improvement, this paper firstly built a recovery rate prediction model with BP neural network, and then worked on this model with the help of LM and POS algorithms respectively. The comparison of simulation shows that, the PSO algorithm can overcome the shortcomings of local minimum and improve the precision of convergence to a certain extent. The simulation results confirmed the high efficiency of this algorithm.
Keywords :
alloy steel; backpropagation; neural nets; particle swarm optimisation; product quality; production engineering computing; steel industry; BP neural network; LM algorithm; POS algorithm; PSO algorithm; alloying elements; alloying model precision; convergence precision improvement; metallurgical industry; prediction model; recovery rate calculation; steel quality improvement; Alloying; Convergence; Mathematical model; Neural networks; Predictive models; Standards; Training; LM Algorithm; Neural Network and Prediction Model; PSO; Recovery Rate of Alloying Elements;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561301
