校准
合并(版本控制)
计算机科学
聚类分析
偏最小二乘回归
化学计量学
人工神经网络
数据挖掘
机器学习
人工智能
数学
统计
情报检索
作者
Helene Fog Froriep Halberg,Anette Yde Holst,Niels Kaufmann,Rasmus Bro
摘要
Abstract Calibration model maintenance is often overlooked but is a significant part of successful use of multivariate calibration models, for example, in process monitoring and optimization. In some cases, companies are maintaining tens or even hundreds of calibration models. This could be partial least squares (PLS) calibration models pertaining to different recipes or raw materials or neural network based models covering different production sites. Maintaining such a high number of models is cumbersome and expensive. Sometimes, a solution presented for this problem is to merge all the models into one, but this often comes at the expense of significantly higher prediction errors. In this paper, we suggest a new approach for rationally merging calibration models in order to optimally balance the prediction error and maintenance workload. We do this by systematically merging models that lower the error as much as possible and hence provide a sort of optimal clustering or fusion of calibration models. We showcase the new approach on a case based on infrared spectroscopy applied to dairy production.
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