Modeling nonlinear dynamical systems using support vector machine

Author

Zhang, Hao-Ran ; Wang, Xiao-Dong ; Zhang, Chang-Jiang ; Xu, Xiu-ling

Author_Institution

Coll. of Inf. Sci. & Eng., Zhejiang Normal Univ., Jinhua, China

Volume

5

fYear

2005

fDate

18-21 Aug. 2005

Firstpage

3204

Abstract

This paper proposes a general framework for modeling nonlinear dynamical systems based on support vector machine (SVM), firstly provides a short introduction to regression SVM, then uses standard support vector machine to model nonlinear dynamical system, and gives a theoretic analysis about its robustness under noise. The simulation results indicate that the SVM method can reduce the effect of sample´s number and noise for modeling, and its performance is better than that of neural network modeling method.

Keywords

nonlinear dynamical systems; regression analysis; robust control; support vector machines; neural network modeling method; nonlinear dynamical systems; regression SVM; robustness; support vector machine; EMP radiation effects; Linear regression; Neural networks; Noise robustness; Nonlinear dynamical systems; Nonlinear systems; Power system modeling; Statistical learning; Support vector machine classification; Support vector machines; modeling; robustness; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on

Conference_Location

Guangzhou, China

Print_ISBN

0-7803-9091-1

Type

conf

DOI

10.1109/ICMLC.2005.1527495

Filename

1527495