摘要
根据150 t转炉的冶炼工艺和生产数据及转炉终点磷含量的影响因素,并针对现有BP网络学习算法的不足,基于BP算法提出一种改进网络训练算法,建立了基于模糊神经网络的转炉终点磷含量的预报模型。结果表明,改进后的模型预报转炉终点磷含量误差为±0．002％的命中率达68．69％,预报误差±0．004％的命中率达95．96％,磷含量的最大误差为±0．006％。
Back-propagation based improved training network algorithm was proposed and a prediction model for converter end point phosphorus based on fuzzy neural network has been established based on melting process and production data of an 150 t converter and analysis on influence factors on end point phosphorus, in accordance with the deficiencies of present back propagation algorithm. The results showed that percentage of hits of converter end point phosphorus content in steel with error ± 0. 002% was 68.69% , that with error ±0. 004% was up to 95.96%, and the maximum error of end point phosphorus content was±0. 006%
出处
《特殊钢》
北大核心
2007年第2期41-43,共3页
Special Steel
关键词
转炉
模糊神经网络
终点磷
预报
Converter, Fuzzy Neural Network, End Point Phosphorus, Prediction
