• TUCS Publication Series
• How to Publish in TUCS Series
• Publication Search
• Publication Graph
• Publication Input
• Publication Input Guide
• JuFo Browser

Identification of State-Dependent Parameter Models with Support Vector Regression

Hannu T. Toivonen, Stefan Tötterman, Bernt Åkesson, Identification of State-Dependent Parameter Models with Support Vector Regression. International Journal of Control 80(9), 1454 – 1470, 2007.

Abstract:

A support vector regression approach is presented for the
identification of state-dependent parameter ARX models, whose
parameters are described as functions of past inputs and outputs. The
problem of identifying the state-dependent parameters reduces to a
standard support vector regression problem with a kernel function which
is defined in terms of the kernels used to represent the individual
parameters. Numerical examples show that the support vector method gives accurate parameter estimates for systems which have a
state-dependent parameter representation.

BibTeX entry:

@ARTICLE{jToToAk07a,
  title = {Identification of State-Dependent Parameter Models with Support Vector Regression},
  author = {Toivonen, Hannu T. and Tötterman, Stefan and Åkesson, Bernt},
  journal = {International Journal of Control},
  volume = {80},
  number = {9},
  pages = {1454 – 1470},
  year = {2007},
  keywords = {identification; kernel methods; nonlinear systems; sampled-data systems},
}

Belongs to TUCS Research Unit(s): Other

Publication Forum rating of this publication: level 1

Edit publication