Enhanced algorithm for randomised model structure selection

L. P. Fagundes, A. S. Morais, L. C. Oliveira-Lopes and J. S. Morais

International Journal of Systems Science, 2022, vol. 53, issue 5, 1090-1109

Abstract: Model structure selection for nonlinear system identification has been widely studied over the last 30 years due to its great importance. There are many methods in the literature to deal with structure selection, although these methods have their specific benefits, they face some difficulties in selecting the structure for a parsimonious model. In this paper, two methods based on the Randomised Model Structure Selection (RaMSS) approach are introduced in order to deal with the structure selection problem. The first one is the Randomised Model Structure Selection with Error Reduction Ratio (RaMSS-ERR) that uses the error reduction ratio as a filter for the terms analysis, improving the convergence, and the second one is the Randomised Model Structure Selection with Genetic Inheritance (RaMSS-EGI) that uses a genetic inheritance in order to get a faster convergence. The methods were applied to benchmark models and the results are encouraging. Applications to systems with a large candidate regressor set and to a continuous stirred-tank reactor are also carried out. The results show that the proposed method may be used to identify both linear and nonlinear model structures with a reduced number of iterations, computational time, and number of explored models.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2021.1988755 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:53:y:2022:i:5:p:1090-1109

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2021.1988755

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().