Mean-Squared-Error Methods for Selecting Optimal Parameter Subsets for Estimation
Mean-Squared-Error Methods for Selecting Optimal Parameter Subsets for Estimation
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DOI：
10.1021/ie202352f
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发表时间：
2012-05-02
影响因子：
4.2
通讯作者：
McAuley, Kimberley B.
中科院分区：
文献类型：
--
作者：
McLean, Kevin A. P.;Wu, Shaohua;McAuley, Kimberley B.
为化学过程开发基本模型的工程师往往无法估计所有参数，特别是在可用数据有限或有噪音的情况下。在这些情况下，建模师可能决定只选择参数的一个子集进行估计。使用正交化算法和基于均方误差(MSE)的选择准则对参数从最大到最小进行排序，并确定应该估计的参数子集以获得最佳预测。提出了一种稳健性检验方法，并将其应用于间歇反应器模型，以评估所选参数子集对初始参数猜测的敏感性。基于MSE准则，提出了一种新的排序和选择方法，并与文献中已有的方法进行了比较。使用所提出的排序和选择技术获得的结果与留一法交叉验证的结果一致，但在计算上更具吸引力。
Engineers who develop fundamental models for chemical processes are often unable to estimate all of the parameters, especially when available data are limited or noisy. In these situations, modelers may decide to select only a subset of the parameters for estimation. An orthogonalization algorithm combined with a mean squared error (MSE) based selection criterion has been used to rank parameters from most to least estimable and to determine the parameter subset that should be estimated to obtain the best predictions. A robustness test is proposed and applied to a batch reactor model to assess the sensitivity of the selected parameter subset to initial parameter guesses. A new ranking and selection technique is also developed based on the MSE criterion and is compared with existing techniques in the literature. Results obtained using the proposed ranking and selection techniques agree with those from leave-one-out cross-validation but are more computationally attractive.