Nonlinear Multivariate Quality Prediction Based on OSC-SVM-PLS

Predicting data with noise and high correlation using latent structural projection or partial least-squares (PLS) is a commonly used linear regression method. In order to solve the nonlinear problem existing in the industrial processes, many methods for extending the PLS to the nonlinear space have been proposed. In this paper, the support vector machine (SVM) is integrated into the PLS to get a nonlinear PLS model. In order to reduce information in process variables that are independent of quality variables, a orthogonal signal correction (OSC) method has been introduced. Nonlinear data from industrial processes is used to build nonlinear PLS models and SVM methods are adopted to approximate nonlinear internal relationships between the input and the output. The model proposed in this paper can predict industrial process quality variables and improve prediction accuracy compared with traditional PLS.