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A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling

Comminiello, Danilo; Nezamdoust, Alireza; Scardapane, Simone; Scarpiniti, Michele; Hussain, Amir; Uncini, Aurelio


Danilo Comminiello

Alireza Nezamdoust

Simone Scardapane

Michele Scarpiniti

Aurelio Uncini


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.

Journal Article Type Article
Acceptance Date Aug 7, 2022
Online Publication Date Sep 16, 2022
Publication Date 2023-03
Deposit Date Sep 28, 2022
Publicly Available Date Oct 24, 2022
Journal IEEE Transactions on Systems, Man, and Cybernetics: Systems
Print ISSN 2168-2216
Electronic ISSN 2168-2232
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 53
Issue 3
Pages 1384-1396
Keywords Efficient adaptive filtering, frequency-domain adaptive filters (FDAFs), functional links, low-complexity algorithms, nonlinear adaptive filters
Public URL


A New Class Of Efficient Adaptive Filters For Online Nonlinear Modeling (accepted version) (1.4 Mb)

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