Predictive Models of Aluminum Reduction Cell Based on LS-SVM

摘要：

Bath temperature and alumina concentration are two important but hard to measure online parameters of aluminum reduction cell. To this problem, a novel method based on least squares support vector machine (LS-SVM) and chaos optimization is proposed to establish predictive models of the two parameters. This method employs chaos optimization technique to iterate and search in feasible regions so as to find optimal LS-SVM algorithm parameters and corresponding model parameters. The simulation results show that this method has smaller absolute error and relative error than those of neural network method.

关键词： aluminum reduction cell bath temperature alumina concentration least squares support vector machine chaos optimization predictive model

作者: Gang YAN Ximing LIANG

作者单位: School of Information Science and Engineering, Central South University, Changsha, 410083, China Dep School of Information Science and Engineering, Central South University, Changsha, 410083, China

会议类型: 国际会议

会议名称: 2010 International Conference on Digital Manufacturing and Automation(2010 数字制造与自动化国际会议 ICDMA 2010)

会议地点: 长沙

会议语种:英文

页码: 99-102

在线出版日期: 2010-12-18（万方平台首次上网日期，不代表论文的发表时间）