Outliers in process modeling and identification
Outliers in process modeling and identification
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DOI：
10.1109/87.974338
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发表时间：
2002-01-01
影响因子：
4.8
通讯作者：
Pearson, RK
中科院分区：
文献类型：
--
作者：
Pearson, RK
基于模型的控制策略（例如模型预测控制 (MPC)）需要足够准确的过程动态模型，以便最终的控制器在实践中能够充分运行。通常，这些模型是通过将方便的模型结构（例如，线性有限脉冲响应（FIR）模型、线性零极点模型、非线性 Hammerstein 或 Wiener 模型等）拟合到观察到的输入输出数据来获得的。实际测量数据记录经常包含“异常值”或“异常数据点”，这可能会严重降低原本合理的经验模型识别过程的结果。本文考虑了一些包含异常值的真实数据集，研究了异常值对线性和非线性系统识别的影响，并讨论了异常值检测和数据清理问题。尽管没有一种策略是普遍适用的，但这里描述的 Hampel 过滤器在实践中通常非常有效。
Model-based control strategies like model predictive control (MPC) require models of process dynamics accurate enough that the resulting controllers perform adequately in practice. Often, these models are obtained by fitting convenient model structures (e.g., linear finite impulse response (FIR) models, linear pole-zero models, nonlinear Hammerstein or Wiener models, etc.) to observed input-output data. Real measurement data records frequently contain "outliers" or "anomalous data points," which can badly degrade the results of an otherwise reasonable empirical model identification procedure. This paper considers some real datasets containing outliers, examines the influence of outliers on linear and nonlinear system identification, and discusses the problems of outlier detection and data cleaning. Although no single strategy is universally applicable, the Hampel filter described here is often extremely effective in practice.