Publication:
Combined nonlinear filtering architectures involving sparse functional link adaptive filters

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.contributor.authorComminielle, Danilo
dc.contributor.authorScarpiniti, Michele
dc.contributor.authorAzpicueta Ruiz, Luis Antonio
dc.contributor.authorArenas García, Jerónimo
dc.contributor.authorUncini, Aurelio
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2022-01-17T10:09:03Z
dc.date.available2022-01-17T10:09:03Z
dc.date.issued2017-06
dc.description.abstractSparsity phenomena in learning processes have been extensively studied, since their detection allows to derive suited regularized optimization algorithms capable of improving the overall learning performance. In this paper, we investigate the sparsity behavior that may occur in nonlinear adaptive filtering problems and how to leverage it and develop enhanced algorithms. In particular, we focus on a particular class of linear-in-the-parameters nonlinear adaptive filters, whose nonlinear transformation is based on a functional link expansion. The analysis of the sparsity in functional links leads us to derive a family of adaptive combined filtering architectures that is capable of exploiting any sparseness degree in the nonlinear filtering. We propose two different filtering schemes based on a new block-based combined approach, well suited for sparse adaptive algorithms. Moreover, a hierarchical architecture is also proposed that generalizes the different combined schemes and does not need any a priori information about the nature of the nonlinearity to be modeled. Experimental results prove the effectiveness of the proposed combined architectures in exploiting any sparseness degree and improving the modeling performance in nonlinear system identification problems.en
dc.description.sponsorshipThe work of M. Scarpiniti and A. Uncini is partially supported by the Italian National Project "GAUChO - A Green Adaptive Fog Computing and Networking Architecture", under grant number 2015YPXH4W. The work of L. A. Azpicueta-Ruiz is partially supported by Comunidad de Madrid under grant 'CASI-CAM-CM' (id. S2013/ICE-2845), by the Spanish Ministry of Economy and Competitiveness (under grant DAMA (TIN2015-70308-REDT) and grant TEC2014-52289-R), and by the European Union. The work of J. Arenas-García has been partly funded by MINECO project TEC2014-52289-R, and by Comunidad de Madrid project PRICAM S2013/ICE-2933.en
dc.format.extent11
dc.identifier.bibliographicCitationComminiello, D., Scarpiniti, M., Azpicueta-Ruiz, L. A., Arenas-García, J. & Uncini, A. (2017). Combined nonlinear filtering architectures involving sparse functional link adaptive filters. Signal Processing, 135, 168–178.en
dc.identifier.doihttps://doi.org/10.1016/j.sigpro.2017.01.009
dc.identifier.issn0165-1684
dc.identifier.publicationfirstpage168
dc.identifier.publicationlastpage178
dc.identifier.publicationtitleSignal Processingen
dc.identifier.publicationvolume135
dc.identifier.urihttps://hdl.handle.net/10016/33885
dc.identifier.uxxiAR/0000019638
dc.language.isoengen
dc.publisherElsevieren
dc.relation.projectIDComunidad de Madrid. S2013/ICE-2845es
dc.relation.projectIDComunidad de Madrid. S2013/ICE-2933es
dc.relation.projectIDGobierno de España. TEC2014-52289-Res
dc.relation.projectIDGobierno de España. TIN2015-70308-REDTes
dc.rights© 2017 Elsevier B.V. All rights reserved.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherNonlinear adaptive filteringen
dc.subject.otherFunctional linksen
dc.subject.otherLinear-in-the-parameters nonlinear filtersen
dc.subject.otherSparse adaptive filtersen
dc.subject.otherCombination of filtersen
dc.subject.otherAcoustic echo cancellationen
dc.subject.otherConvex combinationen
dc.subject.otherChannel estimationen
dc.subject.otherPNLMS algorithmen
dc.subject.otherIdentificationen
dc.subject.otherSystemsen
dc.subject.otherRepresentationen
dc.subject.otherConvergenceen
dc.subject.otherKernelsen
dc.titleCombined nonlinear filtering architectures involving sparse functional link adaptive filtersen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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