Data Prediction in Manufacturing: An Improved Approach Using Least Squares Support Vector Machines

摘要

Support vector machine (SVM) is a set of related supervised learning methods used for classification and regression based on statistical learning theory. In this paper, we present a least squares support vector machines (LSSVM) regression method based on relative error for manufacturing industries to estimate the true value of imprecise measured data during production logistics process. Our method has already been successfully applied in Manufacturing Execution System (MES) of some petrochemical enterprises in China.