Comparisons of Element Yield Rate Prediction Using Feed-Forward Neural Networks and Support Vector Machine

摘要：

For the complexity of ladle furnace refining production process, it’s impossible to establish accurate mathematical prediction model for element yield rate that is an important parameter in the process of alloy adding. Model selection is the key factor of better element yield rate prediction. In this paper, feed-forward neural networks (FNN) and support vector machine (SVM) are chosen as candidate modeling methods. We introduce that, under certain condition, FNN and SVM can be transformed into each other. Then an analysis of the essential difference between two algorithms is carried out. The element yield rate prediction models were set up using different FNN and ε -SVR. The comparison results show that modeling by ε-SVR can meet the production requirements and has better prediction accuracy than by FNN.

关键词： Feed-Forward Neural Networks Support Vector Machine Ladle Furnace Element Yield Rate

作者: Zhe Xu Zhizhong Mao

作者单位: School of Information Science and Engineering, Northeastern University, Shenyang, 110819, China School of Information Science and Engineering, Northeastern University, Shenyang, 110819, China Key

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 4163-4166

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）