Abstract:
A new recursive algorithm for least square support vector regression(RLS-SVR) is proposed.This algorithm has the characteristics of improving the real-time property of LS-SVR and updating rapidly.Moreover a hybrid training-regression framework based on the algorithm of RLS-SVR is also presented.The method takes advantage of the speed of online learning and training of RLS-SVR effectively,and avoids high dimension model that will reduce the prediction precision.A soft sensor model is set up with which the composition in Tennessee Eastman(TE) process is predicted.The validity and feasibility of the presented method are illustrated.