Nonlinear models are known to provide excellent performance in real-world applications that often operate in nonideal conditions. However, such applications often require online processing to be performed with limited computational resources. To address this problem, we propose a new class of efficient nonlinear models for online applications. The proposed algorithms are based on linear-in-the-parameters (LIPs) nonlinear filters using functional link expansions. In order to make this class of functional link adaptive filters (FLAFs) efficient, we propose low-complexity expansions and frequency-domain adaptation of the parameters. Among this family of algorithms, we also define the partitioned-block frequency-domain FLAF (FD-FLAF), whose implementation is particularly suitable for online nonlinear modeling problems. We assess and compare FD-FLAFs with different expansions providing the best possible tradeoff between performance and computational complexity. Experimental results prove that the proposed algorithms can be considered as an efficient and effective solution for online applications, such as the acoustic echo cancellation, even in the presence of adverse nonlinear conditions and with limited availability of computational resources.

A new class of efficient adaptive filters for online nonlinear modeling / Comminiello, Danilo; Nezamdoust, Alireza; Scardapane, Simone; Scarpiniti, Michele; Hussain, Amir; Uncini, Aurelio. - In: IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. SYSTEMS. - ISSN 2168-2232. - 53:3(2023), pp. 1384-1396. [10.1109/TSMC.2022.3202656]

A new class of efficient adaptive filters for online nonlinear modeling

Danilo Comminiello
;
Alireza Nezamdoust;Simone Scardapane;Michele Scarpiniti;Aurelio Uncini
2023

Abstract

Nonlinear models are known to provide excellent performance in real-world applications that often operate in nonideal conditions. However, such applications often require online processing to be performed with limited computational resources. To address this problem, we propose a new class of efficient nonlinear models for online applications. The proposed algorithms are based on linear-in-the-parameters (LIPs) nonlinear filters using functional link expansions. In order to make this class of functional link adaptive filters (FLAFs) efficient, we propose low-complexity expansions and frequency-domain adaptation of the parameters. Among this family of algorithms, we also define the partitioned-block frequency-domain FLAF (FD-FLAF), whose implementation is particularly suitable for online nonlinear modeling problems. We assess and compare FD-FLAFs with different expansions providing the best possible tradeoff between performance and computational complexity. Experimental results prove that the proposed algorithms can be considered as an efficient and effective solution for online applications, such as the acoustic echo cancellation, even in the presence of adverse nonlinear conditions and with limited availability of computational resources.
2023
efficient adaptive filtering; frequency-domain adaptive filters (FDAFs); functional links; low-complexity algorithms; nonlinear adaptive filters
01 Pubblicazione su rivista::01a Articolo in rivista
A new class of efficient adaptive filters for online nonlinear modeling / Comminiello, Danilo; Nezamdoust, Alireza; Scardapane, Simone; Scarpiniti, Michele; Hussain, Amir; Uncini, Aurelio. - In: IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. SYSTEMS. - ISSN 2168-2232. - 53:3(2023), pp. 1384-1396. [10.1109/TSMC.2022.3202656]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1661290
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