Dynamic Least Squares Support Vector Machine

Author

Fan, Yugang ; Li, Ping ; Song, Zhihuan

Author_Institution

Inst. of Ind. Process Control, Zhejiang Univ., Hang Zhou

Volume

1

fYear

0

fDate

0-0 0

Firstpage

4886

Lastpage

4889

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-carboxybenzaldchyde (4-CBA) in purified terephthalic acid (PTA) oxidation process. Results indicate that the proposed method is effective

Keywords

chemical reactors; least squares approximations; nonlinear systems; oxidation; support vector machines; time series; time-varying systems; 4-carboxybenzaldchyde; dynamic least squares support vector machine; nonlinear time-varying system; purified terephthalic acid oxidation process; real time system recognition; time series prediction; Industrial control; Kernel; Least squares methods; Oxidation; Power engineering and energy; Process control; Real time systems; Support vector machine classification; Support vector machines; Training data; PTA oxidation process; least squares support vector machine; time series prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713313

Filename

1713313