Combined nonlinear filtering architectures involving sparse functional link adaptive filters

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Show simple item record Comminielle, Danilo Scarpiniti, Michele Azpicueta Ruiz, Luis Antonio Arenas García, Jerónimo Uncini, Aurelio 2022-01-17T10:09:03Z 2022-01-17T10:09:03Z 2017-06
dc.identifier.bibliographicCitation Comminiello, 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.
dc.identifier.issn 0165-1684
dc.description.abstract Sparsity 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.
dc.description.sponsorship The 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.
dc.format.extent 11
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2017 Elsevier B.V. All rights reserved.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Nonlinear adaptive filtering
dc.subject.other Functional links
dc.subject.other Linear-in-the-parameters nonlinear filters
dc.subject.other Sparse adaptive filters
dc.subject.other Combination of filters
dc.subject.other Acoustic echo cancellation
dc.subject.other Convex combination
dc.subject.other Channel estimation
dc.subject.other PNLMS algorithm
dc.subject.other Identification
dc.subject.other Systems
dc.subject.other Representation
dc.subject.other Convergence
dc.subject.other Kernels
dc.title Combined nonlinear filtering architectures involving sparse functional link adaptive filters
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.rights.accessRights openAccess
dc.relation.projectID Comunidad de Madrid. S2013/ICE-2845
dc.relation.projectID Comunidad de Madrid. S2013/ICE-2933
dc.relation.projectID Gobierno de España. TEC2014-52289-R
dc.relation.projectID Gobierno de España. TIN2015-70308-REDT
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 168
dc.identifier.publicationlastpage 178
dc.identifier.publicationtitle Signal Processing
dc.identifier.publicationvolume 135
dc.identifier.uxxi AR/0000019638
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Teoría de la Señal y Comunicaciones
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