Drezet, P. and Harrison, R.F.
(1998)
Support Vector Machines for System Identification.
Research Report.
ACSE Research Report 704
.
Department of Automatic Control and Systems Engineering
Abstract
Support Vector Machines (SVM's) are used for system identification of both linear and non-linear dynamic systems. Discrete time linear models are used to illustrates parameter estimation and non-linear models demonstrate model structure identification. The VC dimension of a trained SVM indicates the model accuracy without using separate validation data. We conclude that the SVM's have potential in the field of dynamic system identification but that there are a number of significant issues to be addressed.
Metadata
Item Type:
Monograph
Authors/Creators:
Drezet, P.
Harrison, R.F.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
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