Study on method of on-line identification for complex nonlinear dynamic system based on SVM

Jin Long An; Zheng Ou Wang; Qing Xin Yang; Zhen Ping Ma; Chang Ju Gao

doi:10.1109/ICMLC.2005.1527210

Study on method of on-line identification for complex nonlinear dynamic system based on SVM

Jin Long An^*
, Zheng Ou Wang
, Qing Xin Yang
, Zhen Ping Ma
, Chang Ju Gao

^*Corresponding author for this work

Hebei University of Technology
Tianjin University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a kind of regression method with good generalization ability. This paper analyses the disadvantage of the nonlinear dynamical systems identification method based on neural networks, and presents a online support vector machine method to model nonlinear dynamical systems. Theoretical analysis and simulation result indicate that this method has the merits of high learning speed, good generalization as well as approximation ability, and little dependence on samples set. The present method has the better prediction precision than that of the approach based on the neural network.

Original language	English
Title of host publication	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Publisher	IEEE Computer Society
Pages	1654-1659
Number of pages	6
ISBN (Electronic)	0780390911
ISBN (Print)	078039092X, 9780780390928
DOIs	https://doi.org/10.1109/ICMLC.2005.1527210
State	Published - 2005
Externally published	Yes
Event	International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005

Publication series

Name	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Volume	3

Conference

Conference	International Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/Territory	China
City	Guangzhou
Period	18/08/05 → 21/08/05

Keywords

Magnetostriction
Nonlinear Model
On-Line Dynamic System Identification
Support Vector Machine

Access to Document

10.1109/ICMLC.2005.1527210

Cite this

An, J. L., Wang, Z. O., Yang, Q. X., Ma, Z. P., & Gao, C. J. (2005). Study on method of on-line identification for complex nonlinear dynamic system based on SVM. In 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005 (pp. 1654-1659). (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005; Vol. 3). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2005.1527210

@inproceedings{e1e0aac40cfb4a8b87ace826beb038d9,

title = "Study on method of on-line identification for complex nonlinear dynamic system based on SVM",

abstract = "Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a kind of regression method with good generalization ability. This paper analyses the disadvantage of the nonlinear dynamical systems identification method based on neural networks, and presents a online support vector machine method to model nonlinear dynamical systems. Theoretical analysis and simulation result indicate that this method has the merits of high learning speed, good generalization as well as approximation ability, and little dependence on samples set. The present method has the better prediction precision than that of the approach based on the neural network.",

keywords = "Magnetostriction, Nonlinear Model, On-Line Dynamic System Identification, Support Vector Machine",

author = "An, \{Jin Long\} and Wang, \{Zheng Ou\} and Yang, \{Qing Xin\} and Ma, \{Zhen Ping\} and Gao, \{Chang Ju\}",

year = "2005",

doi = "10.1109/ICMLC.2005.1527210",

language = "英语",

isbn = "078039092X",

series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

publisher = "IEEE Computer Society",

pages = "1654--1659",

booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

address = "美国",

note = "International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",

}

An, JL, Wang, ZO, Yang, QX, Ma, ZP & Gao, CJ 2005, Study on method of on-line identification for complex nonlinear dynamic system based on SVM. in 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005, vol. 3, IEEE Computer Society, pp. 1654-1659, International Conference on Machine Learning and Cybernetics, ICMLC 2005, Guangzhou, China, 18/08/05. https://doi.org/10.1109/ICMLC.2005.1527210

Study on method of on-line identification for complex nonlinear dynamic system based on SVM. / An, Jin Long; Wang, Zheng Ou; Yang, Qing Xin et al.
2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. IEEE Computer Society, 2005. p. 1654-1659 (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Study on method of on-line identification for complex nonlinear dynamic system based on SVM

AU - An, Jin Long

AU - Wang, Zheng Ou

AU - Yang, Qing Xin

AU - Ma, Zhen Ping

AU - Gao, Chang Ju

PY - 2005

Y1 - 2005

N2 - Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a kind of regression method with good generalization ability. This paper analyses the disadvantage of the nonlinear dynamical systems identification method based on neural networks, and presents a online support vector machine method to model nonlinear dynamical systems. Theoretical analysis and simulation result indicate that this method has the merits of high learning speed, good generalization as well as approximation ability, and little dependence on samples set. The present method has the better prediction precision than that of the approach based on the neural network.

AB - Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a kind of regression method with good generalization ability. This paper analyses the disadvantage of the nonlinear dynamical systems identification method based on neural networks, and presents a online support vector machine method to model nonlinear dynamical systems. Theoretical analysis and simulation result indicate that this method has the merits of high learning speed, good generalization as well as approximation ability, and little dependence on samples set. The present method has the better prediction precision than that of the approach based on the neural network.

KW - Magnetostriction

KW - Nonlinear Model

KW - On-Line Dynamic System Identification

KW - Support Vector Machine

UR - https://www.scopus.com/pages/publications/28444439875

U2 - 10.1109/ICMLC.2005.1527210

DO - 10.1109/ICMLC.2005.1527210

M3 - 会议稿件

AN - SCOPUS:28444439875

SN - 078039092X

SN - 9780780390928

T3 - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

SP - 1654

EP - 1659

BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

PB - IEEE Computer Society

T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005

Y2 - 18 August 2005 through 21 August 2005

ER -

An JL, Wang ZO, Yang QX, Ma ZP, Gao CJ. Study on method of on-line identification for complex nonlinear dynamic system based on SVM. In 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. IEEE Computer Society. 2005. p. 1654-1659. (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005). doi: 10.1109/ICMLC.2005.1527210

Study on method of on-line identification for complex nonlinear dynamic system based on SVM

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Keywords

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