摘要
针对传统BP神经网络在训练过程中存在收敛速度慢的缺陷,将LM(levenberg marquardt)算法引入到BP神经网络的训练过程,建立了LM-BP神经网络模型,并将其应用于连铸过程中的漏钢预报系统。结合某钢厂连铸现场历史数据对系统进行了测试,测试结果以96.15%的预报率及100%的报出率,验证了基于LM算法的BP神经网络连铸漏钢预报方案的可行性和有效性。
LM algorithm was introduced to the training process of a BP neural network and a LM--BP neural network model was established aiming at the defects of slow convergence in the train- ing process of the traditional BP neural network. The LM--BP neural network model was applied to the breakout prediction in the continuous casting processes, and it was tested with the historical data collected from a steel mill. The feasibility and the validity of the model are verified by the results with the accuracy rate of 96.15% and the prediction rate of 100%
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2012年第2期204-207,共4页
China Mechanical Engineering
基金
河北省科学技术研究与发展计划资助项目(07212119D)
关键词
连铸
漏钢预报
LM算法
BP神经网络
continuous casting
breakout prediction
LM (Levenberg Marquardt) algorithm
BP neural network