@InProceedings{8552955,
  author = {D. Comminiello and M. Scarpiniti and S. Scardapane and L. A. Azpicueta-Ruiz and A. Unclni},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Combined Sparse Regularization for Nonlinear Adaptive Filters},
  year = {2018},
  pages = {336-340},
  abstract = {Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity. Recently, a class of proportionate algorithms has been proposed for nonlinear filters to leverage sparsity of their coefficients. However, the choice of the norm penalty of the cost function may be not always appropriate depending on the problem. In this paper, we introduce an adaptive combined scheme based on a block-based approach involving two nonlinear filters with different regularization that allows to achieve always superior performance than individual rules. The proposed method is assessed in nonlinear system identification problems, showing its effectiveness in taking advantage of the online combined regularization.},
  keywords = {adaptive filters;least mean squares methods;nonlinear filters;norm penalty;cost function;online combined regularization;nonlinear system identification problems;adaptive combined scheme;sparse behavior;nonlinear adaptive filters;sparse regularization;Adaptation models;Adaptive filters;Lips;Europe;Cost function;Indexes;Sparse Regularization;Functional Links;Linear-in-the-Parameters Nonlinear Filters;Sparse Adaptive Filters;Adaptive Combination of Filters},
  doi = {10.23919/EUSIPCO.2018.8552955},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439439.pdf},
}

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A.","Unclni, A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["D."],"propositions":[],"lastnames":["Comminiello"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Scarpiniti"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Scardapane"],"suffixes":[]},{"firstnames":["L.","A."],"propositions":[],"lastnames":["Azpicueta-Ruiz"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Unclni"],"suffixes":[]}],"booktitle":"2018 26th European Signal Processing Conference (EUSIPCO)","title":"Combined Sparse Regularization for Nonlinear Adaptive Filters","year":"2018","pages":"336-340","abstract":"Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity. Recently, a class of proportionate algorithms has been proposed for nonlinear filters to leverage sparsity of their coefficients. 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