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基于极限学习机(ELM)的连铸坯质量预测

陈恒志 杨建平 卢新春 余相灼 刘青

陈恒志, 杨建平, 卢新春, 余相灼, 刘青. 基于极限学习机(ELM)的连铸坯质量预测[J]. 工程科学学报, 2018, 40(7): 815-821. doi: 10.13374/j.issn2095-9389.2018.07.007
引用本文: 陈恒志, 杨建平, 卢新春, 余相灼, 刘青. 基于极限学习机(ELM)的连铸坯质量预测[J]. 工程科学学报, 2018, 40(7): 815-821. doi: 10.13374/j.issn2095-9389.2018.07.007
CHEN Heng-zhi, YANG Jian-ping, LU Xin-chun, YU Xiang-zhuo, LIU Qing. Quality prediction of the continuous casting bloom based on the extreme learning machine[J]. Chinese Journal of Engineering, 2018, 40(7): 815-821. doi: 10.13374/j.issn2095-9389.2018.07.007
Citation: CHEN Heng-zhi, YANG Jian-ping, LU Xin-chun, YU Xiang-zhuo, LIU Qing. Quality prediction of the continuous casting bloom based on the extreme learning machine[J]. Chinese Journal of Engineering, 2018, 40(7): 815-821. doi: 10.13374/j.issn2095-9389.2018.07.007

基于极限学习机(ELM)的连铸坯质量预测

doi: 10.13374/j.issn2095-9389.2018.07.007
基金项目: 

国家自然科学基金资助项目(50874014)

详细信息
  • 中图分类号: TF777.2

Quality prediction of the continuous casting bloom based on the extreme learning machine

  • 摘要: 针对传统基于BP神经网络建立的连铸坯质量预测模型训练速度慢、适应能力弱、预测精度低等问题,本文提出一种基于极限学习机的连铸坯质量预测方法,对方大特钢60Si2Mn连铸坯中心疏松和中心偏析缺陷进行预测,并与BP和遗传算法优化BP神经网络预测模型的预测结果进行分析对比.结果表明:BP及GA-BP神经网络预测模型对连铸坯中心疏松和中心偏析缺陷的预测准确率分别为50%、57.5%、70%和72.5%;而基于极限学习机的连铸坯预测模型预测准确率更高,对连铸坯中心疏松和中心偏析缺陷的预测准确率分别为85%和82.5%,且该模型具有极快的运算时间,仅需0.1 s.该模型可对连铸坯质量进行迅速准确地分析,为连铸坯质量预测的在线应用提供了一种新的方法.
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  • 收稿日期:  2017-06-12

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