Deep Learning-Based Residual Control Chart for Binary Response
| Autor: | Il Do Ha, Jong Min Kim |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
nonlinear PCA
Generalized linear model 0209 industrial biotechnology Physics and Astronomy (miscellaneous) General Mathematics 02 engineering and technology binary data 020901 industrial engineering & automation Statistics QA1-939 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) Control chart Mathematics PCA bayesian variable selection Artificial neural network business.industry Deep learning Regression analysis residual control chart Chemistry (miscellaneous) Multicollinearity Principal component analysis Binary data 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Symmetry Volume 13 Issue 8 Symmetry, Vol 13, Iss 1389, p 1389 (2021) |
| ISSN: | 2073-8994 |
| DOI: | 10.3390/sym13081389 |
| Popis: | A residual (r) control chart of asymmetrical and non-normal binary response variable with highly correlated explanatory variables is proposed in this research. To avoid multicollinearity between multiple explanatory variables, we employ and compare a neural network regression model and deep learning regression model using Bayesian variable selection (BVS), principal component analysis (PCA), nonlinear PCA (NLPCA) or whole multiple explanatory variables. The advantage of our r control chart is able to process both non-normal and correlated multivariate explanatory variables by employing a neural network model and deep learning model. We prove that the deep learning r control chart is relatively efficient to monitor the simulated and real binary response asymmetric data compared with r control chart of the generalized linear model (GLM) with probit and logit link functions and neural network r control chart. |
| Databáze: | OpenAIRE |
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