An improved multi-kernel RVM integrated with CEEMD for high-quality intervals prediction construction and its intelligent modeling application

Most of existing modeling methods are based on point prediction. However, the accuracy of point prediction cannot meet the actual demand due to existence of high noise, volatility, complexity and irregularity inherent in the chemical process data. In order to solve this problem, a hybrid high-quality prediction intervals (PIs) method integrating complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), and improved multi-kernel relevant vector machine (RVM) is proposed in the paper. The proposed PIs method mainly consists of three aspects: Firstly, CEEMD is adopted to decompose the original data into several independent intrinsic mode functions (IMFS), and then SE is used to analyze the complexity of the extracted IMFs to obtain recombinant components; Secondly, an improved multi-kernel RVM (MRVM) is presented to predict recombinant components independently, in which the linear kernel and the Gaussian kernel are combined; Thirdly, the predicted components are aggregated to obtain an ensemble result using another MRVM for constructing the high-quality PIs. To verify the performance of the proposed PIs method, a purified Terephthalic acid (PTA) solvent system is selected. Comparative simulation results demonstrate that the proposed PIs method greatly outperforms on coverage probability and sharpness in all the step predictions.