| 基于证据框架的最小二乘支持向量机在精对苯二甲酸生产中的应用 |
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| 引用本文： | 郑小霞,钱锋.基于证据框架的最小二乘支持向量机在精对苯二甲酸生产中的应用[J].化工学报,2006,57(7):1612-1616. |
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| 作者姓名： | 郑小霞 钱锋 |
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| 作者单位： | 华东理工大学自动化研究所，上海 200237 |
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| 基金项目： | 国家重点基础研究发展计划(973计划) |
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| 摘 要： | 支持向量机是一种基于统计学习理论的新型机器学习方法.本文给出一种考虑损失函数的噪声模型参数β的贝叶斯证据框架最小二乘支持向量机回归算法，通过贝叶斯证据框架自动调整正则化参数和核参数，更好地实现了最小化误差和模型复杂性之间的折中.将提出的算法用于精对苯二甲酸（purified terephthalic acid，PTA）生产过程中的关键指标对羧基苯甲醛（4-carboxybenzaldhyde，4-CBA）含量的预测中，能很好地跟踪4-CBA含量的变化趋势，泛化能力较强，为4-CBA含量的实时预测提供了很好的解决方案.
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| 关 键 词： | 最小二乘支持向量机 贝叶斯 证据框架 对羧基苯甲醛 |
| 文章编号： | 0438-1157（2006）07-1612-05 |
| 收稿时间： | 06 13 2005 12:00AM |
| 修稿时间： | 2005-06-132005-12-15 |
Application of least squares support vector machine within evidence framework in PTA process |
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| ZHENG Xiaoxia,QIAN Feng.Application of least squares support vector machine within evidence framework in PTA process[J].Journal of Chemical Industry and Engineering(China),2006,57(7):1612-1616. |
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| Authors: | ZHENG Xiaoxia QIAN Feng |
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| Affiliation: | Automation Institute, East China University of Science and Technology, Shanghai 200237, China |
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| Abstract: | Support vector machine（SVM）is a new learning machine based on the statistical learning theory.A regression algorithm based on least squares vector machine（LS-SVM）within the Bayesian evidence framework was proposed by considering the noise parameter β.Within the evidence framework，the regularization and kernel parameters could be adjusted automatically，to achieve a better tradeoff between the minimum error and model’s complexity.The proposed method was applied to predict the concentration of 4-carboxybenzaldhyde（4-CBA）in an industrial purified terephthalic acid（PTA）oxidation process.The estimated outputs of LS-SVM match the real values and followed the change of the 4-CBA content very well.The results show that the proposed method provides a new and effective method for on-line 4-CBA prediction. |
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| Keywords: | least squares support vector machine Bayesian evidence framework 4-CBA |
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