Multivariate Regression Method Study Based On Independent Component Analysis

With the help of modern signal processing techniques, based on existing data-driven multivariate statistical forecasting methods of the process predictions methods. This paper studies process prediction methods within the application of data-driven multivariate statisti-cal process predictions methods in the flow industry. Flow industry is an essential part of our national economy. As the process involves high temperature, high pressure and high risk. the importance of its quality prediction is increasingly prominent.Based on the analysis of ICs extracted from the nature of an independent element on the basis of the proposed amendments based on independent component analysis of multi-variable regression method. Regression analysis of the characteristics of components to extract target information included in the model of the abundance of great influence on the accuracy, In this paper, the first to use ICA to extract the amendment to establish a similar independent component of the PCA regression model, Followed by the establishment of the model to improve:The input variables for the characteristics of independent reunification Extraction, Makes extraction of the input variables and objectives of Independent Independent variables largest mutual information, regression model to build. In order to solve nonlinear problems and then introduced in the output space using the nuclear extraction independent component analysis for non-linear regression prediction.The method is applied to the quality prediction of Tennessee—Eastman Process in this paper. The prediction performance of the proposed approach based ICA is compared to PLSR using some process variables examples. It is proved to be effective through the simulate application upon TE process. And use KICA to extract features of target variables to predict the quality of some process. The simulation result shows that the ICA method can capture the nonlinear dynamic features effectively, and predict the process quality successfully.