摘要
利用人工神经网络的辨识理论和方法 ,进行轧制过程数学模型参数的在线辨识与修正 .首先对轧制压力模型和温度模型进行分析 ,得到适于应用神经网络进行辨识和修正的轧制模型函数形式 ,选择并比较最速下降、递推最小二乘及共轭梯度训练算法 ,实现了离线的和在线的仿真与应用 .仿真结果表明 。
A method of identifying and modifying plate rolling model parameters on line with model identification theory using neural network is introduced. Models of rolling force and of temperature were first analyzed to get suitable function styles for identification and modification with neural networks, and several neural network training algorithms, including the one with the steepest gradient, RLS and conjugated gradient algorithm, were chosen and compared. Off line and on line computer emulation and applications were then realized. The results show that the use of neural network in plate rolling process control can greatly improve the precision of model prediction.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2002年第3期311-314,共4页
Transactions of Beijing Institute of Technology
基金
"八五"国家重点科技攻关项目 (85 -72 0 -10 -0 7)
关键词
神经网络
模型辨识
轧制模型
neural network
model identification
rolling model
