L0-Norm Adaptive Volterra Filters. Yazdanpanah, H., Carini, A., & Lima, M. V. S. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 1-5, Sep., 2019.
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Paper doi abstract bibtex
Paper doi abstract bibtex The paper addresses adaptive algorithms for Volterra filter identification capable of exploiting the sparsity of nonlinear systems. While the l1-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 l0-norm.Thus, in this paper, the Geman-McClure function is adopted to approximate the l0-norm and to derive l0-norm adaptiveVolterra 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 l1-norm solutions.
@InProceedings{8903013,
author = {H. Yazdanpanah and A. Carini and M. V. S. Lima},
booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
title = {L0-Norm Adaptive Volterra Filters},
year = {2019},
pages = {1-5},
abstract = {The paper addresses adaptive algorithms for Volterra filter identification capable of exploiting the sparsity of nonlinear systems. While the l1-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 l0-norm.Thus, in this paper, the Geman-McClure function is adopted to approximate the l0-norm and to derive l0-norm adaptiveVolterra 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 l1-norm solutions.},
keywords = {adaptive filters;approximation theory;nonlinear filters;Volterra filter identification;nonlinear systems;coefficient vector;Geman-McClure function;real-world system;norm solutions;L1norm adaptive Volterra filters;Approximation algorithms;Signal processing algorithms;Adaptive systems;Convergence;Europe;Signal processing;Nonlinear systems;Nonlinear adaptive filter;Volterra series;sparsity;l₀-norm;Geman-McClure function},
doi = {10.23919/EUSIPCO.2019.8903013},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533636.pdf},
}Downloads: 0
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