周南,李绍军

• 1:华东理工大学 化工过程先进控制和优化技术教育部重点实验室，上海 200237

摘要(Abstract)：

在化工过程监控领域，Vine Copula模型为描述高维复杂变量之间相依关系提供了一种新的思想，在不降维的基础上直接刻画变量之间复杂的相关关系。传统的Copula函数模型选择方法是基于赤池信息准则（Akaike information criterion，AIC），但是在利用AIC准则时不仅要计算Copula的密度函数，而且边缘分布的拟合效果也直接影响了AIC的取值。本文提出了基于核密度估计的R-Vine Copula（kernel estimation-based R-vine Copula，KRVC）选择方法，并将其应用在化工过程监控领域。通过核密度选择原理得到R-Vine模型，然后利用高密度区域（HDR）与密度分位数表等理论，构建非高斯态广义局部概率指标（GLP）。该方法在TE（Tennessee Eastman）过程中以及醋酸脱水过程中的应用验证了KRVC方法在过程监控中的良好性能。

关键词(KeyWords)： 过程监控;核密度估计;非线性非高斯;R-Vine Copula;广义贝叶斯推断概率指标;高密度区域

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（21676086）；上海市自然科学基金（14ZR1410500）。

作者(Author): 周南,李绍军

DOI: 10.3969/j.issn.1003-9015.2019.02.024

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