|Table of Contents|

Set membership estimation by weighted least squares support vector machines(PDF)

[ISSN:1007-449X/CN:23-1 408/TM]

Issue:
2009年03
Page:
431-435
Research Field:
Publishing date:

Info

Title:
Set membership estimation by weighted least squares support vector machines
Author(s):
CHAI Wei; SUN Xian-fang
Keywords:
nonlinear systems parameter estimation set membership support vector machines weighted least squares
PACS:
TP13
DOI:
-
Abstract:
A set membership estimation method by weighted least squares support vector machines(LS-SVM) was proposed for nonlinear-in-parameter regression models with unknown but bounded errors.A weighted least squares support vector regression(LS-SVR) was solved to build a model which approximated the complex functional relationship between the weighted l∞ norms of the equation-error vectors and the given parameter vectors.Then the approximate feasible parameter set was obtained according to this model and the feasible weighted l∞ norms of the equation-error vectors.In order to evaluate the results of the proposed method,an index reflecting the closeness between the approximate boundary and the true boundary was given.The simulation results show that the proposed method can give approximate boundaries much closer to true boundaries than the method by unweighted LS-SVM.

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Memo

Memo:
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Last Update: 2009-07-09