Wiener Structure Based Modeling and Identifying of Soft Sensor Systems
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摘要: 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.Abstract: 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 identification, and recursive algorithm.
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Key words:
- Soft sensor /
- Wiener structure /
- modeling /
- substep identification /
- recursive algorithm /
- convergence
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