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Estimating the reliability of predictions in locally weighted partial least‐squares modeling
Author(s) -
Kaneko Hiromasa
Publication year - 2021
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3364
Subject(s) - reliability (semiconductor) , standard deviation , standard error , partial least squares regression , euclidean distance , statistics , mathematics , variable (mathematics) , least squares function approximation , computer science , algorithm , data mining , artificial intelligence , mathematical analysis , power (physics) , physics , quantum mechanics , estimator
It is important to predict the reliability of the estimation results given by adaptive soft sensors. In this study, a locally weighted partial least‐squares (LWPLS) method, which is a just‐in‐time‐based adaptive soft sensor, is analyzed, and the reliability of LWPLS modeling is predicted as the standard deviation of the estimated values of an objective variable y . The relationship between the minimum of Euclidean distance (MinED) and the standard deviation of y errors (SDYE) is constructed using training samples, giving the proposed y ‐error model. For the test samples, the MinED from a query to the training samples is input into the y ‐error model, allowing the SDYE for the query to be predicted. The proposed LWPLS model can estimate the y values with associated error bars, which indicate the reliability of the estimated y values. The effectiveness of the proposed method is demonstrated through two case studies using datasets from industrial plants.