文献类型：期刊文献

中文题名：基于ART-SVR的过程建模及在干点软测量中的应用

英文题名：Process modeling based on ART-SVR and its application in dry point soft measurement

作者：吴国庆[1];颜学峰[1]

机构：[1]华东理工大学自动化研究所,上海200237

年份：2008

卷号：59

期号：4

起止页码：927

中文期刊名：化工学报

外文期刊名：CIESC Journal

收录：CSTPCD;;EI(收录号：20082111262145);Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金：国家自然科学基金项目(20506003;20776042);教育部科学技术研究重点项目(106073);国家高技术研究发展计划项目(2007AA04Z164;2007AA04Z171)~~

语种：中文

中文关键词：自适应谐振神经网络;支持向量回归;建模;干点;软测量

外文关键词：adaptive resonance neural networks; support vector regression; modeling; dry point; soft measurement

摘要：针对石油化工生产过程通常呈高度非线性,且生产过程数据呈非连续、具有一定类别特性等特征,提出基于自适应谐振神经网络(adaptive resonance theory,ART)和支持向量回归(support vector regression,SVR)相结合的建模方法(ART-SVR)。首先,基于建模样本,通过ART将样本模式空间分割成若干模式特性相近的子空间;然后,对各子空间分别采用SVR建立各自模型,实现基于样本模式空间分割的"分段"建模。仿真试验和在石脑油干点软测量建模的实际应用表明:ART-SVR模型的拟合精度和预测精度均优于全局SVR模型。
The petrochemical process is highly nonlinear and the observation data of the petrochemical process are non-continuous and have classified characteristics. A novel process modeling method, which combined adaptive resonance theory （ART） with support vector regression （SVR）, was proposed. Firstly, ART was used to separate the input pattern space into several sub-spaces based on a modeling sample. Then, SVR was used to build up each sub-model for each sub-space. The results of simulation experiment and an application in dry point soft measurement of naphtha showed that ART-SVR could reduce the nonlinear degree of the sub-models and its fitting accuracy and prediction accuracy were both better than those of a single SVR model.