基于变分贝叶斯算法的线性变参数系统辨识

doi:10.11949/j.issn.0438-1157.20171563

化工学报 ›› 2018, Vol. 69 ›› Issue (7): 3125-3134.DOI: 10.11949/j.issn.0438-1157.20171563

基于变分贝叶斯算法的线性变参数系统辨识

李寒霜, 赵忠盖, 刘飞

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:2017-11-23 修回日期:2018-02-05 出版日期:2018-07-05 发布日期:2018-07-05
通讯作者: 刘飞
基金资助:
国家自然科学基金项目（61773183，61573169）。

Identification of linear parameter varying systems with variational Bayesian algorithm

LI Hanshuang, ZHAO Zhonggai, LIU Fei

Key Laboratory of Advanced Control for Light Industry Processes, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China

Received:2017-11-23 Revised:2018-02-05 Online:2018-07-05 Published:2018-07-05
Supported by:
supported by the National Natural Science Foundation of China (61773183, 61573169).

摘要/Abstract

摘要：

线性变参数系统（LPV）将多阶段、非线性的过程建模转化为线性多模型的辨识问题，是解决非线性过程建模的一个有效手段。由于实际工业过程存在各种干扰因素，导致被建模系统呈现随机性及模型参数的不确定性。针对这一问题，考虑采用变分贝叶斯（VB）算法对LPV模型进行辨识。该算法首先给定参数相应的先验分布，通过最大化目标函数的下界，从而估计得到参数的后验分布。不仅可实现对参数的点估计，同时量化了估计值的不确定性。针对典型二阶过程和连续搅拌反应釜（CSTR），运用提出的算法进行仿真实验，表明了该贝叶斯估计方法的优越性。

关键词: 非线性过程, 线性变参数系统, 多模型, 变分贝叶斯算法, 参数估计

Abstract:

Linear parameter varying (LPV) method is an effective tool for nonlinear process modeling, which converts modeling of multi-stage nonlinear complex process into identification of multiple linear models. Various disturbance factors in industrial processes result in stochasticity of system modeling and uncertainty of model parameters. Identification of LPV models was studied under the variational Bayesian (VB) framework. After prior probability distributions were assigned to variables and parameters, posterior distributions of these variables and parameters were estimated by maximizing lower limits of objective functions. This full Bayesian system identification approach not only provided point estimates of parameters, but also quantified uncertainty of estimation. Numerical simulation on typical two-stage process and continuous stirred tank reactor (CSTR) demonstrated effectiveness and superiority of the proposed method.

Key words: nonlinear process, linear parameter varying system, multiple models, variational Bayesian algorithm, parameter estimation

中图分类号:

TP301.6

李寒霜, 赵忠盖, 刘飞. 基于变分贝叶斯算法的线性变参数系统辨识[J]. 化工学报, 2018, 69(7): 3125-3134.

LI Hanshuang, ZHAO Zhonggai, LIU Fei. Identification of linear parameter varying systems with variational Bayesian algorithm[J]. CIESC Journal, 2018, 69(7): 3125-3134.

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基于变分贝叶斯算法的线性变参数系统辨识

Identification of linear parameter varying systems with variational Bayesian algorithm

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