GPR Modeling Method Based on Kernel Slow Feature Analysis and Time Delay Estimation

doi:10.16182/j.issn1004731x.joss.17-0389

Abstract

Abstract: A Gaussian process regression modeling method based on kernel slow feature analysis is proposed to deal with the time delay and nonlinear characteristics in industrial processes. The time delay in the industrial data is effectively extracted by fuzzy curve analysis and the optimal time-delay in the data is obtained. The model data is reconstructed based on the optimal delay. The method of kernel slow feature analysis is used to extract the nonlinear features of the reconstructed data. The Gaussian process regression model is established based on the extracted features. The effectiveness and performance of the method are verified by the simulation experiment of the soft measurement of butane concentration at the bottom of the de-butane tower.

Key words: time delay, kernel slow feature analysis, fuzzy curve analysis, Gaussian process regression

CLC Number:

TP273

Peng Huilai, Xiong Weili. GPR Modeling Method Based on Kernel Slow Feature Analysis and Time Delay Estimation[J]. Journal of System Simulation, 2019, 31(8): 1562-1571.

References

[1] Yuan X, Ge Z, Song Z.Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes[J]. Industrial & Engineering Chemistry Research (S0888-5885), 2014, 53(35): 13736-13749.
[2] Petr K, Ratko G, Bogdan G.Review of adaptation mechanisms for data-driven soft sensors[J]. Computers and Chemical Engineering (S0098-1354), 2011, 35(1): 1-24.
[3] Facco P, Doplicher F, Bezzo F, et al.Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process[J]. Journal of Process Control (S0959-1524), 2009, 19(3): 520-529.
[4] Sun K, Liu J, Kang J L, et al.Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote[J]. Journal of Process Control (S0959-1524), 2014, 24(7): 1068-1075.
[5] 刘毅, 王海清. 采用最小二乘支持向量机的青霉素发酵过程建摸研究[J]. 生物工程学报, 2006, 22(1): 144-149.
Liu Yi, Wang Haiqing.Modeling optimization of SVM for a fermentation soft sensor[J]. Chinese Journal of Biotechnology, 2006, 22(1): 144-149.
[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] Zhang W, Li Y, Xiong W, et al.Adaptive soft sensor for online prediction based on enhanced moving window GPR[C]//Control, Automation and Information Sciences (ICCAIS), 2015 International Conference on. Changshu China: IEEE, 2015: 291-296.
[8] 范雪莉, 冯海泓, 原猛. 基于互信息的主成分分析特征选择算法[J]. 控制与决策, 2013, 28(6): 915-919.
Fan Xueli, Feng Haihong, Yuan Meng, et al.PCA based on mutual information for feature selection[J]. Control and Decision, 2013, 28(6): 915-919.
[9] Lee J M, Yoo C K, Lee I B.Statistical process monitoring with independent component analysis[J]. Journal of Process Control (S0959-1524), 2004, 14(5): 467-485.
[10] Zhang Y.Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM[J]. Chemical Engineering Science (S0009-2509), 2009, 64(5): 801-811.
[11] Wiskott L, Sejnowski T J.Slow feature analysis: Unsupervised learning of invariances[J]. Neural computation (S0899-7667), 2002, 14(4): 715-770.
[12] Blaschke T, Zito T, Wiskott L.Independent slow feature analysis and nonlinear blind source separation[J]. Neural computation (S0899-7667), 2007, 19(4): 994-1021.
[13] Shang C, Yang F, Gao X, et al.Extracting latent dynamics from process data for quality prediction and performance assessment via slow feature regression[C]//American Control Conference (ACC), 2015. Chicago, IL, USA: IEEE, 2015: 912-917.
[14] Shang C, Huang B, Yang F, et al.Probabilistic slow feature analysis‐based representation learning from massive process data for soft sensor modeling[J]. AIChE Journal (S0001-1541), 2015, 61(12): 4126-4139.
[15] Zhang Z, Tao D.Slow feature analysis for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2012, 34(3): 436-450.
[16] Shang C, Huang B, Yang F, et al.Slow feature analysis for monitoring and diagnosis of control performance[J]. Journal of Process Control (S0959-1524), 2016, 39: 21-34.
[17] Zhang L, Wu C, Du B.Automatic radiometric normalization for multitemporal remote sensing imagery with iterative slow feature analysis[J]. IEEE Transactions on Geoscience and Remote Sensing (S0196-2892), 2014, 52(10): 6141-6155.
[18] Böhmer W, Grünewälder S, Nickisch H, et al.Regularized sparse kernel slow feature analysis[J]. Machine Learning and Knowledge Discovery in Databases (S0302-9743), 2011: 235-248.
[19] Böhmer W, Grünewälder S, Nickisch H, et al.Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis[J]. Machine Learning (S0885-6125), 2012, 89(1/2): 67-86.
[20] Fortuna L, Graziani S, Rizzo A, et al.Soft sensors for monitoring and control of industrial processes[M]. London: Springer Science & Business Media, 2007.
[21] Zhang J, Jin Q, Xu Y.Inferential estimation of polymer melt index using sequentially trained bootstrap aggregated neural networks[J]. Chemical engineering & technology (S0930-7516), 2006, 29(4): 442-448.
[22] 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.
[23] Lin Y, Cunningham G A.A new approach to fuzzy-neural system modeling[J]. IEEE Transactions on Fuzzy systems (S1063-6706), 1995, 3(2): 190-198.
[24] 蒋昕祎, 杜红彬, 李绍军. 基于核慢特征回归与互信息的常压塔软测量建模[J]. 化工学报, 2017, 68(5): 1977-1986.
Jiang Xinyi, Du Hongbin, Li Shaojun.Atmospheric tower soft sensor based on kernel slow feature regression and mutual information[J]. CIESC Journal, 2017, 68(5): 1977-1986.
[25] 刘瑞兰. 软测量技术若干问题的研究及工业应用[D]. 杭州: 浙江大学, 2004.
Liu Ruilan.Some Studies on Soft Sensor Technology and Their Applications to Industry Process[D]. Hangzhou: Zhejiang University, 2004.
[26] Fortuna L, Graziani S, Xibilia M G.Soft sensors for product quality monitoring in debutanizer distillation columns[J]. Control Engineering Practice (S0967-0661), 2005, 13(4): 499-508.