DocumentCode :
1573378
Title :
Application study on nonlinear dynamic FIR modeling using hybrid SVM-PLS method
Author :
Wang, Huazhong ; Yu, Jinshou
Author_Institution :
Res. Inst. of Autom., East China Univ. of Sci. & Technol., Shanghai, China
Volume :
4
fYear :
2004
Firstpage :
3479
Abstract :
Hybrid support vector machines and partial least squares (SVM-PLS) method for modeling was proposed and was applied to develop nonlinear dynamic finite impulse response (FIR) models in order to improve the performances of the model. Firstly the theory of support vector regression machines and PLS was briefly described. Secondly the principals and framework of hybrid SVM-PLS method were introduced. This method integrated the merits of both SVM and PLS. Thirdly the steps in developing nonlinear FIR model using hybrid SVM-PLS method were given. Finally the superior performances of the nonlinear FIR model were demonstrated by an application study on a chemical process.
Keywords :
chemical industry; identification; least squares approximations; support vector machines; transient response; chemical process; hybrid SVM-PLS method; nonlinear dynamic FIR modeling; partial least squares; support vector machines; Automation; Chemical processes; Finite impulse response filter; Kernel; Least squares methods; Neural networks; Nonlinear dynamical systems; Support vector machine classification; Support vector machines; System identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
Type :
conf
DOI :
10.1109/WCICA.2004.1343192
Filename :
1343192
Link To Document :
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