基于BGMM的即时学习软测量建模方法

doi:10.16182/j.issn1004731x.joss.17-0285

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (8): 1555-1561.doi: 10.16182/j.issn1004731x.joss.17-0285

基于BGMM的即时学习软测量建模方法

祁成¹, 熊伟丽²

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

收稿日期:2017-06-14 修回日期:2017-07-28 发布日期:2019-12-12
作者简介:祁成(1994-),男,安徽滁州,硕士生,研究方向为工业过程建模;熊伟丽(1978-),女,江苏无锡,博士生,教授,博导,研究方向为复杂工业过程建模及优化、软测量技术。
基金资助:
国家自然科学基金(61773182),国家重点研发计划子课题(2018YFC1603705-03)

A Just-in-time Learning Soft Sensing Modeling Method Based on Bayesian Gaussian Mixture Model

Qi Cheng¹, Xiong Weili²

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

Received:2017-06-14 Revised:2017-07-28 Published:2019-12-12

摘要/Abstract

摘要： 对于具有非高斯特性的时变工业过程,一般的软测量方法建立的模型很难满足精度要求。为有效解决上述问题,提出一种基于贝叶斯高斯混合模型(BGMM)的即时学习软测量建模方法。对于给定的训练样本集,利用贝叶斯信息准则对高斯混合模型的成分个数进行优化;对于新的测试样本,利用BGMM相似度准则从训练样本中找出与之最相似的一组样本建立高斯过程回归模型;用该模型对测试样本进行预测。通过脱丁烷塔塔底丁烷浓度的软测量建模仿真,验证了所提方法的有效性。

关键词: 高斯混合模型, 即时学习, 贝叶斯信息准则, 高斯过程回归

Abstract: For some time-varying industrial processes with non-Gaussian properties, the model established by the general soft-sensing method is difficult to meet the accuracy requirement. To solve the above problems effectively, a JITL soft sensor modeling method (BGMM) is proposed based on Bayesian Gaussian Mixture Model. For the given training sample set, the number of components of the Gaussian mixture model is optimized by Bayesian Information Criterion (BIC); For new test samples, Gaussian Process Regression (GPR) model is established by using the BGMM similarity criterion for the training samples to find out the most similar set;The model is used to predict the test samples. The effectiveness of the proposed method is verified by modeling and simulating the concentration of butane at the butane tower bottom .

Key words: Gaussian mixture model, just-in-time learning, Bayesian information criterion, Gaussian process regression

中图分类号:

TP273

祁成, 熊伟丽. 基于BGMM的即时学习软测量建模方法[J]. 系统仿真学报, 2019, 31(8): 1555-1561.

Qi Cheng, Xiong Weili. A Just-in-time Learning Soft Sensing Modeling Method Based on Bayesian Gaussian Mixture Model[J]. Journal of System Simulation, 2019, 31(8): 1555-1561.

参考文献

[1] Liu J, Chen D S, Shen J F.Development of self-validating soft sensors using fast moving window partial least squares[J]. Industrial & Engineering Chemistry Research (S0888-5885), 2010, 49(22): 11530-11546.
[2] Choi D J, Park H.A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process[J]. Water research (S0043-1354), 2001, 35(16): 3959-3967.
[3] Desai K, Badhe Y, Tambe S S, et al.Soft-sensor development for fed-batch bioreactors using support vector regression[J]. Biochemical Engineering Journal (S1369-703X), 2006, 27(3): 225-239.
[4] Ge Zhiqiang.Active probabilistic sample selection for intelligent soft sensing of industrial processes[J]. Chemometrics & Intelligent Laboratory Systems (S0169-7439), 2016, 151: 181-189.
[5] Rasmussen C E.Gaussian processes for machine learning[J]. Lecture Notes in Computer Science (S0129-0657), 2004, 3176: 63-71.
[6] 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 Guangbin, Zhao Xijing, et al.Overview of Gaussian process regression[J]. Control and Decision, 2013, 28(8): 1121-1129.
[7] 王春鹏, 于佐军, 孟凡强. 折息移动窗递推PLS算法及其在聚丙烯生产过程中的应用[J]. 化工学报, 2013, 64(12): 4592-4598.
Wang Chunpeng, Yu Zuojun, Meng Fanqiang.Discount moving window recursive PLS algorithm and its application to process of polypropylene production[J]. Journal of Chemical Industry and Engineering, 2013, 64(12): 4592-4598.
[8] Ge Z, Song Z.A comparative study of just-in-time- learning based methods for online soft sensor modeling[J]. Chemometrics and Intelligent Laboratory Systems (S0169-7439), 2010, 104(2): 306-317.
[9] 李庆良, 雷虎民. 一种基于即时学习的多模型在线建模方法[J]. 四川大学学报(工程科学版), 2010, 42(1): 196-200.
Li Qingliang, Lei Humin.An Online Multiple-model Modeling Method Based on Lazy Learning[J]. Journal of Sichuan University(Engineering Science Edition), 2010, 42(1): 196-200.
[10] Witten I H, Frank E, Hall M A, et al.Data Mining: Practical machine learning tools and techniques[J]. BioMedical Engineering Online (S1475-925X), 2006, 5(1).
[11] 李昌利, 沈玉利. 期望最大算法及其应用(1)[J]. 计算机工程与应用, 2008, 44(29): 61-64.
Li Changli, Shen Yuli.Tutorial of EM algorithm and its application(1)[J]. Computer Engineering and Applications, 2008, 44(29): 61-64.
[12] Shimo-onoda K, Tanaka T, Furushima K, et al. Akaike's information criterion for a measure of linkage disequilibrium[J]. Journal of human genetics (S1434-5161), 2002, 47(12): 0649-0655.
[13] Zanini A, Woodbury A D.Contaminant source reconstruction by empirical Bayes and Akaike's Bayesian Information Criterion[J]. Journal of contaminant hydrology, 2016, 185: 74-86.
[14] Mehrjou A, Hosseini R, Araabi B N.Improved Bayesian information criterion for mixture model selection[J]. Pattern Recognition Letters (S0167-8655), 2016, 69: 22-27.
[15] Han J, Zhang X P, Wang F.Gaussian Process Regression Stochastic Volatility Model for Financial Time Series[J]. IEEE Journal of Selected Topics in Signal Processing (S1941-0484), 2016, 10(6): 1015-1028.
[16] 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]. Journal of Chemical Industry and Engineering, 2014, 65(11): 4497-4502.

基于BGMM的即时学习软测量建模方法

A Just-in-time Learning Soft Sensing Modeling Method Based on Bayesian Gaussian Mixture Model

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