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Regressor and Structure Selection in NARX Models using a Structured ANOVA Approach
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2008 (English)In: Automatica, ISSN 0005-1098, Vol. 44, no 2, 383-395 p.Article in journal (Refereed) Published
Abstract [en]

Regressor selection can be viewed as the first step in the system identification process. The benefits of finding good regressors before estimating complex models are especially clear for nonlinear systems, where the class of possible models is huge. In this article, a structured way of using the tool analysis of variance (ANOVA) is presented and used for NARX model (nonlinear autoregressive model with exogenous input) identification with many candidate regressors.

Place, publisher, year, edition, pages
Elsevier, 2008. Vol. 44, no 2, 383-395 p.
Keyword [en]
Analysis of variance, Nonlinear system identification, Structure identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-47011DOI: 10.1016/j.automatica.2007.06.010OAI: oai:DiVA.org:liu-47011DiVA: diva2:267907
Note

© 2007 Elsevier Ltd. All rights reserved.

Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2013-07-22

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Lind, IngelaLjung, Lennart

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