A bootstrap method for structure detection of NARMAX models

Many systems may be described by NARMAX models using only a few terms. However, depending on the order of the system the number of candidate terms can become very large. Selection of a subset of these candidate terms is necessary for an efficient system description. This is an unresolved issue in sy...

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Bibliographic Details
Published in:International journal of control 2004-01, Vol.77 (2), p.132-143
Main Authors: Kukreja ‡, Sunil L., Galiana, Henrietta L., Kearney, Robert E.
Format: Article
Language:English
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Summary:Many systems may be described by NARMAX models using only a few terms. However, depending on the order of the system the number of candidate terms can become very large. Selection of a subset of these candidate terms is necessary for an efficient system description. This is an unresolved issue in system identification for over-parameterized models. Therefore, in this paper, we develop a bootstrap structure detection (BSD) algorithm as a means of determining the structure of highly over-parameterized models. The performance of this BSD technique was evaluated by using it to estimate the structure of a (1) simple NARMAX model, (2) moderately over-parameterized NARMAX model and (3) highly over-parameterized NARMAX model. The results demonstrate that the BSD algorithm is a robust method for detecting the structure of NARMAX models. This method provides accurate estimates of parameter statistics without relying on assumptions made by traditional procedures and yields a parsimonious description of the system.
ISSN:0020-7179
1366-5820
DOI:10.1080/00207170310001646264