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浙江大学学报(工学版)
J4  2012, Vol. 46 Issue (5): 830-836    DOI: 10.3785/j.issn.1008-973X.2012.05.009
自动化技术、电气工程     
用于高炉铁水质量预报的改进支持向量回归
刘毅1,李平2,高增梁1
1. 浙江工业大学 化工机械设计研究所,浙江 杭州 310032；2. 浙江大学 工业控制研究所,浙江 杭州 310027
Quality prediction of hot metal in blast furnace using improved
support vector regression
LIU Yi1, LI Ping2, GAO Zeng-liang1
1. Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310032,
China;2. Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027,  China
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摘要：

针对建模数据中包含噪声和离群点会降低相应软测量模型准确性的问题,提出一种结合2层变量空间分析的预处理方法.用多变量修剪法在原始变量空间预处理；并提出支持向量聚类（SVC）的预处理方法,将建模数据映射到高维特征空间,构造一超球体来排除离群点.SVC无需像传统预处理方法假设数据服从正态或近似正态分布,更符合实际的高炉过程.预处理后的数据再用支持向量回归建立软测量模型.在一工业高炉铁水硅含量的建模和预报实验结果表明,所提出方法能够更有效排除离群点,且提高了支持向量回归模型的鲁棒性和预报性能.
Abstract:

A novel preprocessing method integrated two-level spaces of process variables was proposed to overcome the effect of noises and outliers in modeling data, which can degrade the performance of the related soft sensor model. The multivariate trimming was first utilized for preprocessing in the primary space. Then, a support vector clustering (SVC) strategy was proposed for outlier detection. The main idea of SVC is to map the data into the feature space and then to find a hypersphere with the minimal radius that contains most of the mapped data. Different from most of traditional preprocessing methods, SVC dose not assume that data are distributed (approximately) normally, and thus is more suitable for industrial ironmaking processes. After SVC-based preprocessing, a support vector regression soft sensor model was built. An experiment study for an industrial blast furnace is investigated and the results show its superiority, including efficient outlier detection, a more robust support vector regression model and better prediction performance, compared to traditional approaches．
出版日期: 2012-05-01
:  TP 301.6  
基金资助:

国家自然科学基金资助项目（61004136）.
作者简介: 刘毅（1982-），男，副教授，主要从事化工过程系统工程的研究. E-mail: yliuzju@zjut.edu.cn
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引用本文:

刘毅,李平,高增梁. 用于高炉铁水质量预报的改进支持向量回归[J]. J4, 2012, 46(5): 830-836.

LIU Yi, LI Ping, GAO Zeng-liang. Quality prediction of hot metal in blast furnace using improved
support vector regression. J4, 2012, 46(5): 830-836.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2012.05.009    或    http://www.zjujournals.com/eng/CN/Y2012/V46/I5/830

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