文献类型：期刊文献

英文题名：Probabilistic Weighted Copula Regression Model With Adaptive Sample Selection Strategy for Complex Industrial Processes

作者：Zhou, Yang[1];Ren, Xiang[2];Li, Shaojun[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Rutgers State Univ, Dept Chem & Biochem Engn, Piscataway, NJ 08854 USA

年份：2020

卷号：16

期号：11

起止页码：6972

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20203409061871);WOS:【SCI-EXPANDED(收录号:WOS:000554904700023)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 21676086 and in part by the Fundamental Research Funds for the Central Universities under Grant 222201717006. Paper no. TII-19-4704.

语种：英文

外文关键词：Probabilistic logic; Estimation; Training; Adaptation models; Data models; Probability density function; Distribution functions; Active learning; industrial process; Monte Carlo estimation; probabilistic generative model; probabilistic graphical tool; soft sensor; vine copula

摘要：With complicated structures and complex physicochemical reactions, most industrial processes are intrinsically characterized by high dimensionality, nonlinearity, non-Gaussianity, and self-correlation. In this article, a novel probabilistic generative model, called weighted copula regression (WCR), is developed for complex processes. This method employs a probabilistic graphical tool (vine copula) to flexibly handle the underlying patterns via the factorization of complex dependence structures into multiple bivariate copulas. Monte Carlo estimation is incorporated for establishing a fast and reliable computing framework. To avoid overinterpretation of the industrial data, an adaptive sample selection strategy is proposed to explore the underlying distribution of the sample space and to select the most "informative" samples. By considering the copula weights that carry crucial information on the local data, the probabilistic WCR method can provide fast point estimate with prediction uncertainty for every predicted sample. The proposed WCR method is compared with six state-of-the-art methods, and the efficiency is validated using a numerical example and the ethylene cracking furnace process.