Novel SVD integrated with GBDT based Virtual Sample Generation and Its Application in Soft Sensor

Abstract

With the coming of the big data era, data-driven based modeling approaches have become the hot research topic in recent years. Unfortunately, due to the limitation of the actual process, the data is basically in a steady state and it is difficult to obtain enough high-quality data, which is defined as the small sample (SS) problem. Recently, to deal with the SS problem, a virtual sample generation (VSG) approach based on the distribution of the original data has been taken into account. In this paper, a VSG method based on singular value decomposition (SVD) feature decomposition and gradient boosting decision tree (GBDT) prediction model (SVD-GBDT) is proposed. In the proposed SVD-GBDT method, firstly, the distribution characteristics of the original data are used to extract the main features and expand the number of samples by using the SVD algorithm. Then the GBDT algorithm is used to find the virtual output of the virtual samples by the SVD method. Finally, SVD and GBDT are combined to complete the sample expansion (SVD-GBDT-VSG). In this paper, we choose the purified terephthalic acid (PTA) industrial process to verify the effectiveness of the proposed methodology. Simulation results show that compared with related methods, the proposed SVD-GBDT-VSG algorithm in this paper can achieve sample expansion well and at the same time can effectively improve the accuracy performance of soft measurement.