Regularized basis function estimation of Volterra kernels for the cascaded tanks benchmark

Title: Regularized basis function estimation of Volterra kernels for the cascaded tanks benchmark
Creator: Stoddard, Jeremy G.; Welsh, James S.
Relation: 18th IFAC Symposium on System Identification SYSID 2018. 18th IFAC Symposium on System Identification SYSID 2018: Proceedings [presented in IFAC-PapersOnLine, Vol. 51, Issue 15] (Stockholm, Sweden 09-11 July, 2018) p. 413-418
Publisher Link: http://dx.doi.org/10.1016/j.ifacol.2018.09.180
Publisher: International Federation of Automatic Control (IFAC)
Resource Type: conference paper
Date: 2018
Description: In the nonlinear setting, nonparametric estimation methods are convenient because they do not require a detailed model structure selection and can be used with limited prior knowledge on the system of interest. In this paper, we consider the cascaded tanks benchmark dataset, and estimate Volterra series models using a regularized basis function approach. By directly regularizing the basis function expansions of each Volterra kernel in a Bayesian framework, the resulting model has a more compact form and can be estimated far more quickly than the equivalent time domain method, while achieving comparable prediction accuracy with respect to the validation data.
Subject: system identification; nonlinear systems; regularization
Identifier: http://hdl.handle.net/1959.13/1392390
Identifier: uon:33394
Identifier: ISSN:2405-8963
Language: eng
Full Text
Reviewed

		Thumbnail	File	Description	Size	Format
View Details Download			ATTACHMENT02	Author final version	455 KB	Adobe Acrobat PDF	View Details Download