首页 | 官方网站   微博 | 高级检索  
     

中厚板轧制过程智能化温度预报模型
引用本文:谭文,刘振宇,支颖,吴迪,郑芳,王巍.中厚板轧制过程智能化温度预报模型[J].钢铁研究学报,2006,18(12):23-0.
作者姓名:谭文  刘振宇  支颖  吴迪  郑芳  王巍
作者单位:1. 东北大学轧制技术及连轧自动化国家重点实验室,辽宁,沈阳,110004
2. 宝山钢铁股份有限责任公司研究院,上海,201900
摘    要: 采用有限元(FEM)程序模拟计算了中厚板轧制过程中的温度变化,得到与实测温度符合甚好的模拟结果。以模拟计算结果为基础,建立了BP神经网络和回归温度预报模型。采用两种模型对中厚板热轧过程中轧件表面温度变化情况进行了预报。结果表明,神经元网络模型的预报值较回归模型更接近FEM模拟计算值和实测值,可将神经元网络模型应用于中厚板轧制过程中轧件表面温度变化的在线预报。

关 键 词:BP神经网络模型  回归模型  在线温度预报  中厚板轧制
文章编号:1001-0963(2006)12-0023-04
收稿时间:1900-01-01;
修稿时间:2005-04-25

Intelligent Temperature Prediction Model for Plate Rolling
TAN Wen,LIU Zhen-yu,ZHI Ying,WU Di,ZHENG Fang,WANG Wei.Intelligent Temperature Prediction Model for Plate Rolling[J].Journal of Iron and Steel Research,2006,18(12):23-0.
Authors:TAN Wen  LIU Zhen-yu  ZHI Ying  WU Di  ZHENG Fang  WANG Wei
Affiliation:1. State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110004, Liaoning, China; 2. Research Institute, Baoshan Iron and Steel Co Ltd, Shanghai 201900, China
Abstract:The temperature variation during plate rolling was simulated by using FEM. The simulation results were in good agreement with the measured values. BP neural network and Regression models were built based on the simulation results, and predictions about temperature variation were carried out by using the two models. The results showed that the neural network model was more accurate than the regression model, indicating that the neural network model can be applied to on-llne temperature prediction during plate rolling.
Keywords:BP neural network model  regression model  on-line temperature prediction  plate rolling
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《钢铁研究学报》浏览原始摘要信息
点击此处可从《钢铁研究学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号