| Soft sensor model derived from Wiener model structure: modeling and identification |
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| 作者姓名： | 曹鹏飞 罗雄麟 |
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| 作者单位： | Research Institute of Automation, China University of Petroleum, Beijing 102249, China |
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| 基金项目： | 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）. |
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| 摘 要： | 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 gradi-ent 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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| 关 键 词： | soft sensor Wiener model modeling alternate identification |
| 收稿时间： | 2013-06-09 |
Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification |
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| CAO Pengfei,LUO Xionglin.Soft sensor model derived from Wiener model structure: modeling and identification[J].Chinese Journal of Chemical Engineering,2014,22(5):538-548. |
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| Authors: | CAO Pengfei LUO Xionglin |
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| Affiliation: | Research Institute of Automation, China University of Petroleum, Beijing 102249, China |
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| 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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| Keywords: | soft sensor Wiener model modeling alternate identification |
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