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.

中文翻译：

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基于深度学习的二元响应残差控制图

本研究提出了具有高度相关解释变量的非对称和非正态二元响应变量的残差（r）控制图。为了避免多个解释变量之间的多重共线性，我们使用贝叶斯变量选择 (BVS)、主成分分析 (PCA)、非线性 PCA (NLPCA) 或整体多个解释变量，采用并比较神经网络回归模型和深度学习回归模型。我们的r控制图的优点是能够通过采用神经网络模型和深度学习模型来处理非正态和相关的多元解释变量。我们证明深度学习r控制图是相对高效相比监视模拟和实际二进制响应非对称数据- [R与概率和分对数链路功能和神经网络的广义线性模型（GLM）的控制图ř控制图。