Dynamic Least Squares Support Vector Machine

Abstract

Based on narrating the theory of least squares support vector machine (LS-SVM), dynamic LS-SVM (DLS-SVM) is presented in this paper. DLS-SVM is suitable for real time system recognition and time series prediction. Whenever a new example is obtained, the method gets rid of the first vector and replaces it with the new input vector. That is, this algorithm can adjust the model to track the dynamics of the nonlinear time-varying system. Time series prediction can be a very useful tool to forecast and to study the behavior of key process parameters in time. This creates the possibility to give early warnings of possible process malfunctioning. In this paper, DLS-SVM is applied to predict the concentration of 4-Carboxybenzaldchydc (4-CBA) in purified terephthalic acid (PTA) oxidation process. Results indicate that the proposed method is effective.