一种分层集成的高斯过程回归软测量建模方法

doi:10.16182/j.issn1004731x.joss.17-0374

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (10): 2042-2051.doi: 10.16182/j.issn1004731x.joss.17-0374

一种分层集成的高斯过程回归软测量建模方法

赵帅¹, 史旭东¹, 熊伟丽^1,2

1. 江南大学物联网工程学院自动化研究所,江苏无锡 214122;
2. 江南大学轻工过程先进控制教育部重点实验室,江苏无锡 214122

收稿日期:2017-08-04 修回日期:2017-08-28 出版日期:2019-10-10 发布日期:2019-12-12
作者简介:赵帅(1992-),男,安徽淮北,硕士生,研究方向为工业过程建模;史旭东(1993-),男,江苏常州,硕士生,研究方向为工业过程建模。
基金资助:
国家自然科学基金(61773182), 江苏高校优势学科建设工程资助项目(PAPD)

A Hierarchical Integrated Soft Sensing Modeling Method for Gauss Process Regression

Zhao Shuai¹, Shi Xudong¹, Xiong Weili^1,2

1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China

Received:2017-08-04 Revised:2017-08-28 Online:2019-10-10 Published:2019-12-12

摘要/Abstract

摘要： 针对一些化工过程呈现显著的非线性和多阶段特点,提出一种分层集成的高斯过程回归软测量建模方法。采用高斯混合模型将过程数据划分为不同的操作阶段;对各阶段的数据进行主元分析,依据各辅助变量在主元空间上的贡献度,将各阶段数据划分成若干子空间并建立相应的高斯过程回归模型;再对子空间模型输出进行均值融合,得到第一层集成输出;采用后验概率对各阶段局部预测进行融合,得到第二层集成输出。通过对工业数据的实验仿真,验证了所提方法的有效性。

关键词: 高斯混合模型, 子空间PCA, 高斯过程回归, 分层集成, 软测量

Abstract: Chemical processes are often characterized by nonlinearity and multi-phase, a soft sensor model based on the hierarchical ensemble of Gaussian process regression is proposed. First, the Gaussian mixture model is used to divide the process data into different operation phases. Then, the principal component analysis of each stage is carried out, and the model data are divided into several subspaces, according to the contribution of each auxiliary variable in the principal component space, and the corresponding Gaussian process regression model is built. The subspace model output is fused by means to obtain the first level ensemble output. Finally, the posterior probability is used to fuse the model local prediction to obtain the second level ensemble output. The validity of the proposed method is verified by the experimental simulation of industrial data.

Key words: Gaussian mixture model, subspace PCA, Gaussian process regression, hierarchical ensemble, soft sensing

中图分类号:

TP274

赵帅, 史旭东, 熊伟丽. 一种分层集成的高斯过程回归软测量建模方法[J]. 系统仿真学报, 2019, 31(10): 2042-2051.

Zhao Shuai, Shi Xudong, Xiong Weili. A Hierarchical Integrated Soft Sensing Modeling Method for Gauss Process Regression[J]. Journal of System Simulation, 2019, 31(10): 2042-2051.

参考文献

[1] Kadlec P, Gabrys B, Strandt S.Data-driven soft sensors in the process industry[J]. Computers & Chemical Engineering (S0098-1354), 2009, 33(4): 795-814.
[2] Shao W, Tian X.Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models[J]. Chemical Engineering Research and Design (S0263-8762), 2015, 95: 113-132.
[3] Zhu J, Ge Z, Song Z.Robust supervised probabilistic principal component analysis model for soft sensing of key process variables[J]. Chemical Engineering Science (S0009-2509), 2015, 122: 573-584.
[4] Ge Z, Huang B, Song Z.Mixture semisupervised principal component regression model and soft sensor application[J]. AIChE Journal (S0001-1541), 2014, 60(2): 533-545.
[5] Wang Z X, He Q, Wang J.Comparison of different variable selection methods for partial least squares soft sensor development[C]. American Control Conference (ACC), 2014. Portland, USA: IEEE, 2014: 3116-3121.
[6] Galicia H J, He Q P, Wang J.A reduced order soft sensor approach and its application to a continuous digester[J]. Journal of Process Control (S0959-1524), 2011, 21(4): 489-500.
[7] Gonzaga J C B, Meleiro L A C, Kiang C, et al. ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process[J]. Computers & Chemical Engineering (S0098-1354), 2009, 33(1): 43-49.
[8] Forouzantabar A, Talebi H A, Sedigh A K.Adaptive neural network control of bilateral teleoperation with constant time delay[J]. Nonlinear Dynamics (S0924-090X), 2012, 67(2): 1123-1134.
[9] 张倩, 杨耀权. 基于支持向量机回归的火电厂烟气含氧量软测量[J]. 信息与控制, 2013, 42(2): 258-263.
Zhang Qian, Yang Yaoquan.Soft sensor for Oxygen Content in Flue Gas of Coal-fired Power Plant Based on SVR[J]. Information and Control, 2013, 42(2): 258-263.
[10] Yu J.A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses[J]. Computers & Chemical Engineering (S0098-1354), 2012, 41: 134-144.
[11] Xiong W, Zhang W, Xu B, et al.JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy[J]. Computers & Chemical Engineering (S0098-1354), 2016, 90: 260-267.
[12] Liu Y, Chen T, Chen J.Auto-switch Gaussian process regression-based probabilistic soft sensors for industrial multigrade processes with transitions[J]. Industrial & Engineering Chemistry Research (S0888-5885), 2015, 54(18): 5037-5047.
[13] Jin H, Chen X, Wang L, et al.Adaptive soft sensor development based on online ensemble Gaussian process regression for nonlinear time-varying batch processes[J]. Industrial & Engineering Chemistry Research (S0888-5885), 2015, 54(30): 7320-7345.
[14] 张子迎, 周明全, 税午阳, 等. 基于K均值聚类和区域匹配的颜色迁移技术[J]. 系统仿真学报, 2015, 27(10): 2359-2364.
Zhang Ziying, Zhou Mingquan, Shui Wuyang, et al.Color Transfer Based on K-means Clustering Algorithm and Region Matching[J]. Journal of System Simulation, 2015, 27(10): 2359-2364.
[15] 嵇小辅, 张翔. 基于FCM与集成高斯过程回归的赖氨酸发酵软测量[J]. 智能系统学报, 2015, 10(1): 156-162.
Ji Xiaofu, Zhang Xiang.Soft measurement of lysine fermentation based on FCM and integrated Gaussian process regression[J]. CAAI Transactions on Intelligent Systems, 2015, 10(1): 156-162.
[16] Yu J.Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes[J]. Industrial & Engineering Chemistry Research (S0888-5885), 2012, 51(40): 13227-13237.
[17] Chen T, Ren J.Bagging for Gaussian process regression[J]. Neurocomputing (S0925-2312), 2009, 72(7): 1605-1610.
[18] Cao D S, Xu Q S, Liang Y Z, et al.The boosting: A new idea of building models[J]. Chemometrics and Intelligent Laboratory Systems (S0169-7439), 2010, 100(1): 1-11.
[19] Li T, Chen Y.Fuzzy Clustering Ensemble with Selection of Number of Clusters[J]. Journal of Computers (S1796-203X), 2010, 5(7): 1112-1119.
[20] Ge Z, Song Z.Subspace partial least squares model for multivariate spectroscopic calibration[J]. Chemometrics and Intelligent Laboratory Systems (S0169-7439), 2013, 125: 51-57.
[21] Wang L, Jin H, Chen X, et al.Soft sensor development based on the hierarchical ensemble of Gaussian process regression models for nonlinear and non-Gaussian chemical processes[J]. Industrial & Engineering Chemistry Research (S0888-5885), 2016, 55(28): 7704-7719.
[22] 张伟, 熊伟丽, 徐保国. 基于实时学习的高斯过程回归多模型融合建模[J]. 信息与控制, 2015, 44(4): 487-492,498.
Zhang Wei, Xiong Weili, Xu Baoguo.Multi-model Combination Modeling Based on Just-in-time Learning Using Gaussian Process Regression[J]. Information and Control, 2015, 44(4): 487-492,498.
[23] Yu J.Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach[J]. Chemical Engineering Science (S0009-2509), 2012, 82: 22-30.
[24] 何志昆, 刘光斌, 赵曦晶, 等. 高斯过程回归方法综述[J]. 控制与决策, 2013, 28(8): 1121-1129.
He Zhikun, Liu Guangbing, Zhao Xijing, et al.Overview of Gaussian process regression[J]. Control and Decision, 2013, 28(8): 1121-1129.
[25] Jin H, Chen X, Wang L, et al.Adaptive soft sensor development based on online ensemble Gaussian process regression for nonlinear time-varying batch processes[J]. Industrial & Engineering Chemistry Research (S0888-5885), 2015, 54(30): 7320-7345.
[26] 阮宏镁, 田学民, 王平. 基于联合互信息的动态软测量方法[J]. 化工学报, 2014, 65(11): 4497-4502.
Ruan Hongmei, Tian Xuemin, Wang Ping.Dynamic soft sensor method based on joint mutual information[J]. CIESC Journal, 2014, 65(11): 4497-4502.
[27] Liu Y, Wang H.Pensim simulator and its application in penicillin fermentation process[J]. Journal of System Simulation (S1004-731X), 2006, 12(12): 3524-3527.
[28] 熊伟丽, 姚乐, 徐保国. 基于EM 算法的青霉素发酵过程多阶段融合建模[J]. 化工学报, 2014, 65(12): 4935-4941.
Xiong Weile, Yao Le, Xu Baoguo.Multi-stage fusion modeling for penicillin fermentation process based on EM algorithm[J]. CIESC Journal, 2014, 65(12): 4935-4941.

一种分层集成的高斯过程回归软测量建模方法

A Hierarchical Integrated Soft Sensing Modeling Method for Gauss Process Regression

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