An SVM-based On-line Modeling Method and Its Application

In order solve the problems in the application of v support vector regression(v-SVR) to on-line modeling,a support vector regression on-line modeling method is proposed.Bayesian evidence framework is used to optimize the model parameters.Through determining whether the new observation satisfies the original KKT conditions and assigning different weighting factors to the historical data,the latest data can be used sufficiently,and the model can be refreshed on-line as time passes by.The proposed approach is successfully applied to predict the concentration of 4-carboxybenzaldhyde(4-CBA) in industrial purified terephthalic acid(PTA) oxidation process.The results indicate that the proposed method can track the trend of 4-CBA and it is an effective method for on-line modeling.