Journal article
Finite-sample system identification: An overview and a new correlation method
A Care, BC Csaji, MC Campi, E Weyer
IEEE Control Systems Letters | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Published : 2018
Abstract
Finite-sample system identification algorithms can be used to build guaranteed confidence regions for unknown model parameters under mild statistical assumptions. It has been shown that in many circumstances these rigorously built regions are comparable in size and shape to those that could be built by resorting to the asymptotic theory. The latter sets are, however, not guaranteed for finite samples and can sometimes lead to misleading results. The general principles behind finite-sample methods make them virtually applicable to a large variety of even nonlinear systems. While these principles are simple enough, a rigorous treatment of the attendant technical issues makes the corresponding ..
View full abstractGrants
Awarded by Australian Research Council
Funding Acknowledgements
Manuscript received March 6, 2017; revised May 22, 2017; accepted June 12, 2017. Date of publication June 28, 2017; date of current version July 21, 2017. The work of A. Care was supported in part by the European Research Consortium for Informatics and Mathematics (ERCIM), and in part by the Australian Research Council (ARC) under Grant DP130104028. The work of B. Cs. Csaji was supported in part by the Hung. Sci. Res. Fund (OTKA) under Grant 113038 and Grant GINOP-2.3.2-15-2016-00002, and in part by the Janos Bolyai Research Fellowship under Grant BO/00217/16/6. The work of M. C. Campi was supported by the University of Brescia (in Italian: Universita degli Studi di Brescia) under the project H&W "Clafite." The work of E. Weyer was supported by the ARC under Grant DP130104028. Recommended by Senior Editor R. S. Smith. (Corresponding author: Algo Care.)