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.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2008年第4期927-933,共7页
CIESC Journal
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)~~
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
adaptive resonance neural networks
support vector regression
modeling
dry point
soft measurement
