Combining KPCA with LSSVM for the Mooney-Viscosity Forecasting

Abstract

Least squares support vector machine (LSSVM) has been used in soft sensor modeling in recent years. In developing a successful model based on LSSVM, the first important step is feature extraction. Principal components analysis (PCA) is a usual method for linear feature extraction and kernel PCA (KPCA) is a nonlinear PCA developed by using the kernel method. KPCA can efficiently extract the nonlinear relationship between original inputs. This paper proposes to combine KPCA with LSSVM to forecast the Mooney-viscosity of styrene butadiene rubber (SBR). KPCA is firstly applied for feature extraction. Then LSSVM is applied to proceed regression modeling. The experiment results show that KPCA-LSSVM features high learning speed, good approximation and generalization ability compared with SVM and PCA-SVM. The root mean square errors of the Mooney-viscosity in the KPCA-LSSVM, PCA-LSSVM and LSSVM are 0.0145, 0.0377 and 0.1775 respectively. LSSVM with KPCA for feature extraction has best performance. It may be used to efficiently guide production.