Show simple item record

dc.contributor.authorJin, Jen
dc.contributor.authorYuan, Yen
dc.contributor.authorPan, Wen
dc.contributor.authorTomlin, Cen
dc.contributor.authorWebb, Alexen
dc.contributor.authorGonçalves, Jen
dc.date.accessioned2018-09-05T11:06:32Z
dc.date.available2018-09-05T11:06:32Z
dc.date.issued2017-06-28en
dc.identifier.isbn9781509028733en
dc.identifier.issn0743-1546
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/279141
dc.description.abstract© 2017 IEEE. This paper considers a parametric approach to infer sparse networks described by nonlinear ARX models, with linear ARX treated as a special case. The proposed method infers both the Boolean structure and the internal dynamics of the network. It considers classes of nonlinear systems that can be written as weighted (unknown) sums of nonlinear functions chosen from a fixed basis dictionary. Due to the sparse topology, coefficients of most groups are zero. Besides, only a few nonlinear terms in nonzero groups contribute to the internal dynamics. Therefore, the identification problem should estimate both group-and element-sparse parameter vectors. The proposed method combines Sparse Bayesian Learning (SBL) and Group Sparse Bayesian Learning (GSBL) to impose both kinds of sparsity. Simulations indicate that our method outperforms SBL and GSBL when these are applied alone. A linear ring structure network also illustrates that the proposed method has improved performance to the kernel approach.
dc.titleIdentification of nonlinear sparse networks using sparse Bayesian learningen
dc.typeConference Object
prism.endingPage6486
prism.publicationDate2017en
prism.publicationName2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017en
prism.startingPage6481
prism.volume2018-Januaryen
dc.identifier.doi10.17863/CAM.26521
dcterms.dateAccepted2017-09-01en
rioxxterms.versionofrecord10.1109/CDC.2017.8264636en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-06-28en
dc.contributor.orcidWebb, Alex [0000-0003-0261-4375]
rioxxterms.typeConference Paper/Proceeding/Abstracten
rioxxterms.freetoread.startdate2019-01-18


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record