Concentration Prediction of 4-CBA based on Local Weighted LS-SVM

摘要：

In this paper, a new fuzzy adaptive local modeling method based on local learning and weighted least squares support vector machine (LS-SVM) is proposed by building fuzzy membership model for the training data. Just as LS-SVM, local LS-SVM is also sensitive to outliers or noises. A proper fuzzy membership model based on support vector data description (SVDD) is proposed to deal with the problem. Fuzzy membership value to each input sample is confirmed according to its distance to the center of smallest enclosing hypersphere determined by SVDD. The proposed local weighted LS-SVM is applied to predict the concentration of 4-Carboxybenzaldchydc (4-CBA) in purified terephthalic acid (PTA) oxidation process. Results indicate that the proposed method actually reduces the effect of outliers and its accuracy is improved.

关键词： hast squares support vector machine PTA oxidation process support vector data description

作者: Yugang Fan Hua Wang Haiqing Wang Jiande Wu

作者单位: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kun Faculty of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming, National Lab of Industrial Control Technology & Institute of Industrial Process Control, Zhejiang Un

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: 422-425

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）