Study of nonlinear system identification based on support vector machine

Author

Zhang, Ming-Guang ; Yan, Wei-Wu ; Yuan, Zhan-Ting

Author_Institution

Sch. of Electr. & Inf. Eng., Lanzhou Univ. of Technol., China

Volume

5

fYear

2004

Firstpage

3287

Abstract

System identification plays an important role in control field. Support vector machine (SVM) is a novel machine learning method, and is powerful for the problem with small sample, nonlinearity, high dimension, and local minima. SVM has high generalization. In this paper, nonlinear system identification based on SVM was discussed and corresponding simulation was implemented. Cross validation method is used to select hyperparameter of SVM model. Good result indicates that SVM is effective tool for nonlinear system identification.

Keywords

generalisation (artificial intelligence); identification; learning (artificial intelligence); nonlinear systems; support vector machines; SVM model; cross validation method; generalization; hyperparameter selection; machine learning method; nonlinear system identification; support vector machine; Control systems; Lagrangian functions; Learning systems; Nonlinear systems; Power system modeling; Signal processing algorithms; Statistical learning; Support vector machine classification; Support vector machines; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on

Print_ISBN

0-7803-8403-2

Type

conf

DOI

10.1109/ICMLC.2004.1378604

Filename

1378604