By Topic

Consistent Nonparametric Estimation of NARX Systems Using Convex Optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Roll, J. ; Division of Automatic Control, Linköpings universitet, SE-58183 Linköping, Sweden. e-mail: roll@isy.liu.se ; Enqvist, M. ; Ljung, L.

In this paper, a nonparametric method based on quadratic programming (QP) for identification of nonlinear autoregressive systems with exogenous inputs (NARX systems) is presented. We consider a mixed parametric/nonparametric model structure. The output is assumed to be the sum of a parametric linear part and a nonparametric Lipschitz continuous part. The consistency of the estimator is shown assuming only that an upper bound on the true Lipschitz constant is given. In addition, different types of prior knowledge about the system can easily be incorporated. Examples show that the method can give accurate estimates also for small data sets and that the estimate of the linear part sometimes can be improved compared to the linear least squares estimate.

Published in:

Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on

Date of Conference:

12-15 Dec. 2005