基于Wiener结构的软测量模型及辨识算法

Wiener模型结构能有效地表征系统的动态和静态特性,因此这里首先基于这一结构建立软测量模型,利用动态与静态子模型分别建立辅助变量与主导变量间的动态与静态关系,并说明该软测量模型的可行性,给出模型具体表达式.其次,针对所提模型,提出分步辨识方式获得最优模型参数,说明其可行性.再次,为了减少计算和实现模型在线更新,这里提出参数辨识递推算法,并给出软测量模型参数的收敛性结论.通过实例仿真,可以看出本文提出模型的可行性,以及分步辨识方式与递推算法的有效性....

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Bibliographic Details
Published in自动化学报 Vol. 40; no. 10; pp. 2179 - 2192
Main Author 曹鹏飞 罗雄麟
Format Journal Article
LanguageChinese
Published 中国石油大学 北京 自动化系 北京 102249 2014
Subjects
Wiener结构
分步辨识
建模
收敛性
软测量
递推算法
substep identification
Wiener结构
Soft sensor
分步辨识
modeling
convergence
递推算法
recursive algorithm
收敛性
软测量
建模
Wiener structure
Online AccessGet full text
ISSN0254-4156
1874-1029
DOI10.3724/SP.J.1004.2014.02179

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Summary:Wiener模型结构能有效地表征系统的动态和静态特性,因此这里首先基于这一结构建立软测量模型,利用动态与静态子模型分别建立辅助变量与主导变量间的动态与静态关系,并说明该软测量模型的可行性,给出模型具体表达式.其次,针对所提模型,提出分步辨识方式获得最优模型参数,说明其可行性.再次,为了减少计算和实现模型在线更新,这里提出参数辨识递推算法,并给出软测量模型参数的收敛性结论.通过实例仿真,可以看出本文提出模型的可行性,以及分步辨识方式与递推算法的有效性.
Bibliography:Soft sensor, Wiener structure, modeling, substep identification, recursive algorithm, convergence
CAO Peng-Fei LUO Xiong-Lin 1. Department of Automation, China University of Petroleum, Beijing 102249
The dynamic and static characteristics of system can be effectively described by Wiener model structure. Therefore, a new soft sensor model using this structure is proposed. The dynamic and static relationships between secondary and primary variables are built respectively by dynamic and static submodels. For this soft sensor model, a feasibility analysis is conducted, and a specific model expression is also given. Then, a substep way for identifying the soft sensor model is proposed. In order to reduce the calculation amount and update the model online, a recursive algorithm for identifying the parameters of dynamic and static submodels is proposed, and followed by the convergence conclusion for parameter estimations is shown later. Case study results confirm the effectiveness of the proposed model, substep identi
ISSN:0254-4156
1874-1029
DOI:10.3724/SP.J.1004.2014.02179