Online Identification of Time-varying Processes Using Just-in-time Recursive Kernel Learning Approach
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摘要: 为了及时跟踪非线性化工过程的时变特性, 提出即时递推核学习 (Kernel learning, KL)的在线辨识方法. 针对待预测的新样本点, 采用即时学习 (Just-in-time kernel learning, JITL)策略, 通过构造累积相似度因子, 选择与其相似的样本集建立核学习辨识模型. 为避免传统即时学习对每个待预测点都重新建模的繁琐, 利用两个临近时刻相似样本集的异同点, 采用递推方法有效添加新样本, 并删减旧模型的样本, 以快速建立新即时模型. 通过一时变连续搅拌釜式反应过程的在线辨识, 表明了所提出方法在保证计算效率的同时, 较传统递推核学习方法提高了辨识的准确程度, 能更好地辨识时变过程.
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关键词:
- 过程辨识 /
- 即时学习 /
- 核学习 /
- 最小二乘支持向量回归 /
- 递推辨识
Abstract: An online identification method using just-in-time recursive kernel learning (KL) is proposed to trace the time-varying characteristics of nonlinear chemical processes. For each query sample, a just-in-time kernel learning (JITL) model is established using the similar set constructed by a presented cumulative similarity factor. Different from traditional just-in-time learning approaches discarding their models at each time, an efficient modeling strategy is proposed to reduce the computational load by utilizing the similarity between two neighborhood models. Consequently, a new just-in-time kernel learning model can be quickly constructed using the recursive updating algorithm, by introducing new samples and deleting different samples. The superiority of the proposed online identification method is demonstrated by a continuous stirred tank reactor process with time-varying parameters, showing better prediction performance compared with conventional recursive kernel learning approaches. -
[1] Ljung L, Hjalmarsson H, Ohlsson H. Four encounters with system identification. European Journal of Control, 2011, 17(5): 449-471[2] Himmelblau D M. Accounts of experiences in the application of artificial neural networks in chemical engineering. Industrial and Engineering Chemistry Research, 2008, 47(16): 5782-5796[3] Wang L X. Adaptive Fuzzy Systems and Control: Design and Stability Analysis. New Jersey: Prentice-Hall, 1994.[4] Scholkopf B, Smola A J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press, 2002.[5] Suykens J A K, van Gestel T, de Brabanter J, De Moor B, Vandewalle J. Least Squares Support Vector Machines. Singapore: World Scientific, 2002.[6] Rojo-Alvarez J L, Martinez-Ramon M, de Prado-Cumplido M, Artes-Rodriguez A, Figueiras-Vidal A R. Support vector method for robust ARMA system identification. IEEE Transactions on Signal Processing, 2004, 52(1): 155-164[7] Toivonen H T, Totterman S, Akesson B. Identification of state-dependent parameter models with support vector regression. International Journal of Control, 2007, 80(9): 1454-1470[8] Totterman S, Toivonen H T. Support vector method for identification of Wiener models. Journal of Process Control, 2009, 19(7): 1174-1181[9] Li C H, Zhu X J, Cao G Y, Sui S, Hu M R. Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines. Journal of Power Sources, 2008, 175(1): 303-316[10] Wang H, Pi D Y, Sun Y X. Online SVM regression algorithm-based adaptive inverse control. Neurocomputing, 2007, 70(3): 952-959[11] Tang H S, Xue S T, Chen R, Sato T. Online weighted LS-SVM for hysteretic structural system identification. Engineering Structures, 2006, 28(12): 1728-1735[12] Liu Y, Wang H Q, Yu J, Li P. Selective recursive kernel learning for online identification of nonlinear systems with NARX form. Journal of Process Control, 2010, 20(2): 181-194[13] Cheng C, Chiu M S. A new data-based methodology for nonlinear process modeling. Chemical Engineering Science, 2004, 59(13): 2801-2810[14] Pan T H, Li S Y, Cai W J. Lazy learning-based online identification and adaptive PID control: a case study for CSTR process. Industrial and Engineering Chemistry Research, 2007, 46(2): 472-480[15] Liu Yi. Research on Kernel Learning Adaptive Modeling and Control for Industrial Batch Processes [Ph.D. dissertation], Zhejiang University, China, 2009(刘毅. 间歇过程的核学习自适应建模与控制研究及工业应用 [博士学位论文]. 浙江大学, 中国, 2009)[16] Fujiwara K, Kano M, Hasebe S, Takinami A. Soft-sensor development using correlation-based just-in-time modeling. AIChE Journal, 2009, 55(7): 1754-1765[17] Liu Y Q, Huang D P, Li Y. Development of interval soft sensors using enhanced just-in-time learning and inductive confidence predictor. Industrial and Engineering Chemistry Research, 2012, 51(8): 3356-3367[18] Ge Z Q, Song Z H. A comparative study of just-in-time-learning based methods for online soft sensor modeling. Chemometrics and Intelligent Laboratory Systems, 2010, 104(2): 306-317[19] Golub G H, van Loan C F. Matrix Computations. Baltimore: The John Hopkins University Press, 1996.[20] Nikravesh M, Farell A E, Stanford T G. Control of nonisothermal CSTR with time varying parameters via dynamic neural network control (DNNC). Chemical Engineering Journal, 2000, 76(1): 1-16[21] Gao Z W, Dai X W, Breikin T, Wang H. Novel parameter identification by using a high-gain observer with application to a gas turbine engine. IEEE Transactions on Industrial Informatics, 2008, 4(4): 271-279
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