ELM Based LF Temperature Prediction Model and Its Online Sequential Learning

摘要：

The accurate prediction of molten steel temperature is important for optimal control of Ladle furnace (LF) process. Under this conception, a novel LF temperature prediction model is constructed based on extreme learning machine (ELM), which is a new learning algorithm for single hidden layer feedforward neural networks (SLFNs). ELM is chose due to its good generalization performance and extremely fast learning speed. Furthermore, online sequential learning is adopted on the sequentially arriving data to correct the ELM based prediction model. We introduce a forgetting factor in this learning scheme for the sake of successfully accommodate to the variation in the production process. The simulation results show that the proposed predictor has a good accuracy and fast sequential learning speed, which ensure its ability for practical application.

关键词： ELM SLFNs online sequential learning LF temperature prediction model

作者: LV Wu MAO Zhizhong JIA Mingxing

作者单位: Institute of Information Science and Engineering, Northeastern University, Shenyang 110004, China Institute of Information Science and Engineering, Northeastern University, Shenyang 110004, China St

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 2374-2377

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）