Nonlinear system identification based on SVR with quasi-linear kernel

Yu Cheng*, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

In recent years, support vector regression (SVR) has attracted much attention for nonlinear system identification. It can solve nonlinear problems in the form of linear expressions within the linearly transformed space. Commonly, the convenient kernel trick is applied, which leads to implicit nonlinear mapping by replacing the inner product with a positive definite kernel function. However, only a limited number of kernel functions have been found to work well for the real applications. Moreover, it has been pointed that the implicit nonlinear kernel mapping is not always good, since it may faces the potential over-fitting for some complex and noised learning task. In this paper, explicit nonlinear mapping is learnt by means of the quasi-ARX modeling, and the associated inner product kernel, which is named quasi-linear kernel, is formulated with nonlinearity tunable between the linear and nonlinear kernel functions. Numerical and real systems are simulated to show effectiveness of the quasi-linear kernel, and the proposed identification method is also applied to microarray missing value imputation problem.

本文言語English
ホスト出版物のタイトル2012 International Joint Conference on Neural Networks, IJCNN 2012
DOI
出版ステータスPublished - 2012 8 22
イベント2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
継続期間: 2012 6 102012 6 15

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks

Conference

Conference2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
国/地域Australia
CityBrisbane, QLD
Period12/6/1012/6/15

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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