Covariance-based locally weighted partial least squares for high-performance adaptive modeling
Covariance-based locally weighted partial least squares for high-performance adaptive modeling
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DOI：
10.1016/j.chemolab.2015.05.007
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发表时间：
2015-08-15
影响因子：
3.9
通讯作者：
Kano, Manabu
中科院分区：
文献类型：
--
作者：
Hazama, Koji;Kano, Manabu
局部加权偏最小二乘（LW-PLS）是实时（JIT）建模方法之一；每次需要估计输出变量时，采用PLS建立局部线性回归模型。局部模型的预测精度很大程度上取决于新获得的样本与数据库中存储的过去样本之间的相似性的定义。为了计算相似度，欧几里得距离和马氏距离被广泛使用，但它们没有考虑输入和输出变量之间的关系。这一事实限制了LW-PLS和其他局部权重回归方法的可实现性能。因此，本文提出了基于协方差的局部加权PLS (CbLW-PLS)，该方法将LW-PLS与基于输入和输出变量协方差的新的相似度指标相结合。CbLW-PLS应用于两个工业问题：用于估计石化过程中碱洗涤塔中未反应的NaOH浓度的软传感器设计，以及用于估计制药过程中残留药物浓度的过程分析技术（PAT）。将提出的相似性指数与基于距离、相关性或回归系数的六种常规指数进行比较。结果表明，CbLW-PLS在这两个案例中都取得了最好的预测效果。(C) 2015年作者。这是一篇基于CC by许可协议（http://creativecommons.org/licenses/by/4.0/）的开放获取文章。
Locally weighted partial least squares (LW-PLS) is one of Just-in-Time (JIT) modeling methods; PLS is used to build a local linear regression model every time when output variables need to be estimated. The prediction accuracy of local models strongly depends on the definition of similarity between a newly obtained sample and past samples stored in a database. To calculate the similarity, the Euclidean distance and the Mahalanobis distance have been widely used, but they do not take account of the relationship between input and output variables. This fact limits the achievable performance of LW-PLS and other locally weight regression methods. Thus, in the present work, covariance-based locally weighted PLS (CbLW-PLS) is proposed by integrating LW-PLS and a new similarity index based on the covariance between input and output variables. CbLW-PLS was applied to two industrial problems: soft-sensor design for estimating unreacted NaOH concentration in an alkali washing tower in a petrochemical process, and process analytical technology (PAT) for estimating concentration of a residual drug substance in a pharmaceutical process. The proposed similarity index was compared with six conventional indexes based on distances, correlations, or regression coefficients. The results have demonstrated that CbLW-PLS achieved the best prediction performance of all in both case studies. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license license (http://creativecommons.org/licenses/by/4.0/).