Blind Nonlinear System Identification Based on LS-SVM

ZHU Yan-fei; TAN Hong-zhou; ZHANG Yun

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Blind Nonlinear System Identification Based on LS-SVM

更新时间：2026-05-27

- Blind Nonlinear System Identification Based on LS-SVM
- Vol. 24, Issue 2, Pages: 76-79(2007)
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- Online First：02 July 2007，
  
  Published：2007
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朱燕飞, 谭洪舟, 章云. 基于LS-SVM的非线性系统盲辨识[J]. 广东工业大学学报, 2007, 24(2): 76-79.

ZHU Yan-fei, TAN Hong-zhou, ZHANG Yun. Blind Nonlinear System Identification Based on LS-SVM[J]. 2007, 24(2): 76-79.
朱燕飞, 谭洪舟, 章云. 基于LS-SVM的非线性系统盲辨识[J]. 广东工业大学学报, 2007, 24(2): 76-79. DOI：

ZHU Yan-fei, TAN Hong-zhou, ZHANG Yun. Blind Nonlinear System Identification Based on LS-SVM[J]. 2007, 24(2): 76-79. DOI：

摘要

针对现今在非线性系统盲辨识研究中遇到的困难

提出了一种基于最小二乘支持向量机(LS-SVM)的非线性系统盲辨识方法.该方法直接对非线性系统输出进行过采样

运用LS-SVM非线性建模技术

并结合输入的分布特性

从而完成非线性系统的盲辨识.介绍了盲系统辨识问题的研究内容及过采样技术原理

对LS-SVM的盲系统辨识机理和算法步骤进行了阐述.仿真结果表明了该方法在解决非线性系统盲辨识问题上的切实可行性.

Abstract

To solve the nonlinear problems in blind system identification

a novel blind nonlinear system identification approach based on Least-Square Support Vector Machines(LS-SVM) is investigated in this paper.By oversampling the system outputs

more information of the system characteristics can be observed to blindly identify nonlinear systems.The LS-SVM based mathematical approximation provides an adequate modeling of the unknown system given the distribution knowledge of the system inputs.The paper de...

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references

J.A.K. Suykens,J. Vandewalle. Least Squares Support Vector Machine Classifiers[J], 1999

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