L-0-Norm Adaptive Volterra Filters

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Author(s):	Yazdanpanah, Hamed ; Carini, Alberto ; Lima, Markus V. S. ; IEEE Total Authors: 4
Document type:	Journal article
Source:	2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO); v. N/A, p. 5-pg., 2019-01-01.
Abstract
The paper addresses adaptive algorithms for Volterra filter identification capable of exploiting the sparsity of nonlinear systems. While the l(1)-norm of the coefficient vector is often employed to promote sparsity, it has been shown in the literature that superior results can be achieved using an approximation of the l(0)-norm. Thus, in this paper, the Geman-McClure function is adopted to approximate the l(0)-norm and to derive l(0)-norm adaptive Volterra filters. It is shown through experimental results, also involving a real-world system, that the proposed adaptive filters can obtain improved performance in comparison with classical approaches and l(1)-norm solutions. (AU)

FAPESP's process:	19/06280-1 - Integration, transformation, dataset augmentation and quality control for intermediate representation
Grantee:	Hamed Yazdanpanah
Support Opportunities:	Scholarships in Brazil - Post-Doctoral


FAPESP's process:	15/22308-2 - Intermediate representations in Computational Science for knowledge discovery
Grantee:	Roberto Marcondes Cesar Junior
Support Opportunities:	Research Projects - Thematic Grants