Set-Theoretic Measures as Evolutionary Fitness Criteria in Nonlinear System Identification**This work was supported in part by the U.S. National Science Foundation under Cooperative Agreement DBI-0939454. Any opinions, conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the NSF

Autor:	John R. Deller, Jinyao Yan
Rok vydání:	2015
Předmět:	Nonlinear autoregressive exogenous model Identification (information) Nonlinear system Mathematical optimization Nonlinear system identification Control and Systems Engineering Control theory Model selection Parametric model Evolutionary algorithm System identification Mathematics
Zdroj:	IFAC-PapersOnLine. 48:178-183
ISSN:	2405-8963
DOI:	10.1016/j.ifacol.2015.12.121
Popis:	The paper reports a method for identification of parametric models that are linear and time-invariant in parameters, but arbitrarily nonlinear in signals. Set-bounding solutions are exploited to simultaneously identify and parametrize the model structure. Measures of set-solution quality are used as fitness measures in evolving the structure. Experiments verify effective NARX and NARMAX identification in complex unmodeled disturbances.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a6f6ef0282e2f2b0c718fa7398172b0e https://doi.org/10.1016/j.ifacol.2015.12.121 Zobrazit plný text záznamu