A new approach for online adaptive modeling using incremental support vector regression

Considering the performance of a predictive model is heavily depended on its training samples,a new on-line adaptive modeling approach based on incremental support vector regression (SVR) is presented.When a new sample arrives,it is firstly checked by the Karush-Kuhn-Tucker(KKT) condition of established model,only those which contain sufficient new information can be introduced into the training sample set.In this way,the model generalization ability will be maintained while the update frequency can be reduced.If the new sample cannot be described by the established model and,therefore,has a large prediction error,the model must be updated and the useless sample should be deleted from the model,to adapt the process characteristics.In this case,the useless sample,while not the oldest one,is selectively deleted from the model based on the similarity between samples.The proposed method is illustrated through the application to an industrial polypropylene unit to predict its melt index.The results show that,compared with other methods,the proposed method exhibits good generalization ability while the update frequency significantly lower,and therefore trace the process characteristics effectively.