Multi-model D-vine copula regression model with vine copula-based dependence description

Soft sensor modeling is a popular method to predict the key variables that are not easy to measure in chemical processes. Since the actual process is often a large-scale, multi-working condition, nonlinear, and non-Gaussian complex system, a single soft sensor model cannot fully extract all the information of the process, and the prediction accuracy is relatively low. To address this problem, a multi-model D vine copula regression model with vine copula-based dependence description (VCDD-MCR) is proposed in this paper. This method first divides the training samples into multiple subsets with repetitive samples, and uses the vine copula-based dependence description (VCDD) model to characterize the distributions of these subsets. Then, a generalized local probability (GLP) index is used to determine the location of every training sample among these distributions. If a training sample is at the edges of all distributions, a new subset centered on this sample will be created. Furthermore, a D -vine copula regression model is established for each subset to predict the key variable. The proposed method can handle large-scale, nonlinear, non-Gaussian systems well. The effectiveness of the proposed method is demonstrated using a numerical example and an industrial example.