首页 | 本学科首页   官方微博 | 高级检索  
     

基于RBF网络的钢坯温度预报软测量模型研究
引用本文:姜磊,王德慧,朱里红.基于RBF网络的钢坯温度预报软测量模型研究[J].热加工工艺,2009,38(21).
作者姓名:姜磊  王德慧  朱里红
作者单位:姜磊(浙江工业职业技术学院,浙江,绍兴,312000);王德慧,朱里红(长治钢铁(集团)有限公司,山西,长治,046031) 
基金项目:国家科技支撑计划项目 
摘    要:针对目前的测温技术难以用仪器直接测量出加热炉内被加热钢坯温度的问题,提出了通过RBF神经网络建立钢坯温度软测量预报模型,实现了钢坯温度准确及时预报,达到了减少燃料和钢坯表面氧化的目的.工业试验仿真研究表明,该钢坯温度预报模型精度高、自适应性好、鲁棒性强.

关 键 词:RBF网络  钢坯温度  软测量

Study on Soft Sensor Prediction Model for Slab Temperature Based on RBF Neural Network
Abstract:It's difficult to measure temperature of slab by some measuring instruments directly in the heating furnace now. A soft sensor prediction model for slab temperature was put forward based on RBF neural network. The targets which reduce materials and the probability of oxidation on the slab were attained by forecasting temperature accurately and timely. Industry experiments and simulations indicate that the temperature prediction model of slab is high precision, good adaptive ability and strong robustness.
Keywords:RBF neural network  temperature prediction model of slab  soft sensor
本文献已被 万方数据 等数据库收录!
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