Modeling of the Polluting Emissions from a Cement Production Plant by Partial Least-Squares, Principal Component Regression, and Artificial Neural Networks

2006 ◽  
Vol 40 (1) ◽  
pp. 272-280 ◽  
Author(s):  
Emilio Marengo ◽  
Marco Bobba ◽  
Elisa Robotti ◽  
Maria Cristina Liparota
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component Regression ◽  
Principal Component ◽  
Production Plant ◽  
Cement Production ◽  
Artificial Neural ◽  
Polluting Emissions

scholarly journals Application Of L1- Regularization Approach In QSAR Problem. Linear Regression And Artificial Neural Networks

2019 ◽  
Vol 14 (2) ◽  
pp. 79-90
Author(s):  
M.I. Berdnyk ◽  
A.B. Zakharov ◽  
V.V. Ivanov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Least Squares ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Regression Equations ◽  
Absolute Deviation ◽  
Internal Validation ◽  
Artificial Neural

One of the primary tasks of analytical chemistry and QSAR/QSPR researches is building of prognostic regression equations based on descriptors sets. The one of the most important problems here is to decrease the number of descriptors in the initial descriptor set which is usually way too big. In current investigation the descriptor set is proposed to be reduced employing the least absolute shrinkage and selection operator (LASSO) approach. Decreased descriptor sets were used for calculations with application of the following QSAR/QSPR methods: ordinary least squares (OLS), the least absolute deviation (LAD) regressions and artificial neural networks (ANN). Contrary to aforementioned methods principal component regression (PCR) and partial least squares (PLS) approaches can produce solutions containing numerous descriptors. In this article we compared the viability of these two different descriptor handling ideologies in application to molecular chemical and physical properties prediction. From the obtained results it is possible to see that there are tasks for which PCR and PLS approaches can fail to produce accurate regression equations. At the same time, methods OLS and LAD that use small amount of descriptors can provide viable solutions for the same cases. It was shown that these small sets of descriptors selected with LASSO approach can be used in ANN to obtain models with even better internal validation characteristics.

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