Robust identification of non-linear dynamic systems using support vector machine

被引：17

作者：

Zhang, H. R. ^{[1
]}

Wang, X. D. ^{[1
]}

Zhang, C. J. ^{[1
]}

Cai, X. S. ^{[1
]}

机构：

[1] Zhejiang Normal Univ, Coll Informat Sci & Engn, Jinhua 321004, Peoples R China

来源：

IEE PROCEEDINGS-SCIENCE MEASUREMENT AND TECHNOLOGY | 2006年 / 153卷 / 03期

关键词：

D O I：

10.1049/ip-smt:20050004

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The paper proposes a general framework for modelling non-linear dynamic systems based on a support vector machine (SVM): it first provides a short introduction to regression SVMs, then uses a standard SVM to model a non-linear auto-regressive and moving average (NARMAX) model, and contains a theoretical discussion about its robustness under low and high noise by its properties. The simulation results indicate that the SVM method can reduce the effect of samples and noise for modelling, and its performance is better than that of the neural network modelling method.

引用

页码：125 / 129

页数：5

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