Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification

曹鹏飞; 罗雄麟

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Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification

催化、动力学与反应工程 | Updated：2026-01-06

- Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification
- Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification
- 中国化学工程学报（英文版） 2014年第5期页码：538-5481004-9541
- Affiliations：
  
  Research Institute of Automation, China University of Petroleum,Beijing,China,102249
- Author bio：
- Funds：
  
  Supported by the National Natural Science Foundation of China (61104218, 21006127), the National Basic Research Program of China (2012CB720500) and the Science Foundation of China University of Petroleum (YJRC-2013-12).
- DOI：
  中图分类号：
- 纸质出版：2014
- Accepted：
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曹鹏飞, 罗雄麟. Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification[J]. 中国化学工程学报（英文版）, 2014,(5):538-5481004-9541.

CAO Pengfei, LUO Xionglin. Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification[J]. Chinese Journal of Chemical Engineering, 2014, (5): 538-5481004-9541.
曹鹏飞, 罗雄麟. Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification[J]. 中国化学工程学报（英文版）, 2014,(5):538-5481004-9541. DOI：

CAO Pengfei, LUO Xionglin. Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification[J]. Chinese Journal of Chemical Engineering, 2014, (5): 538-5481004-9541. DOI：

摘要

Abstract

The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However

such integration limits the estimation accuracy of soft sensor models. Wiener model effectively describes dynamic and static characteristics of a system with the structure of dynamic and static submodels in cascade. We propose a soft sensor model derived from Wiener model structure

which is an extension of Wiener model. Dynamic and static relationships between secondary and primary variables are built respectively to describe the dynamic and static characteristics of system. The feasibility of this model is verified. Then the expression of discrete model is derived for soft sensor system. Conjugate gradient algorithm is applied to identify the dynamic and static model parameters alternately. Corresponding update method for soft sensor system is also given. Case studies confirm the effectiveness of the proposed model

alternate identification algorithm

and update method.

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