Linear-in-the-parameters 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, proportionate algorithms have been proposed to leverage sparsity behaviors in nonlinear filtering. In this paper, we deal with this problem by introducing a proportionate adaptive algorithm based on an ℓ1-norm penalty of the cost function, which regularizes the solution, to be used for a class of nonlinear filters based on functional links. The proposed algorithm stresses the difference between useful and useless functional links for the purpose of nonlinear modeling. Experimental results clearly show faster convergence performance with respect to the standard (i.e., non-regularized) version of the algorithm.

Sparse functional link adaptive filter using an ℓ1-norm regularization / Comminiello, D.; Scarpiniti, M.; Scardapane, S.; Uncini, A.. - 2018-:(2018), pp. 1-5. (Intervento presentato al convegno 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 tenutosi a Florence, Italy) [10.1109/ISCAS.2018.8351345].

Sparse functional link adaptive filter using an ℓ1-norm regularization

Comminiello D.;Scarpiniti M.;Scardapane S.;Uncini A.
2018

Abstract

Linear-in-the-parameters 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, proportionate algorithms have been proposed to leverage sparsity behaviors in nonlinear filtering. In this paper, we deal with this problem by introducing a proportionate adaptive algorithm based on an ℓ1-norm penalty of the cost function, which regularizes the solution, to be used for a class of nonlinear filters based on functional links. The proposed algorithm stresses the difference between useful and useless functional links for the purpose of nonlinear modeling. Experimental results clearly show faster convergence performance with respect to the standard (i.e., non-regularized) version of the algorithm.
2018
2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Nonlinear filter; linear-in-the-parameters; functional link filter; echo cancellation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Sparse functional link adaptive filter using an ℓ1-norm regularization / Comminiello, D.; Scarpiniti, M.; Scardapane, S.; Uncini, A.. - 2018-:(2018), pp. 1-5. (Intervento presentato al convegno 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 tenutosi a Florence, Italy) [10.1109/ISCAS.2018.8351345].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1296850
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