Research Output
A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling
  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.

  • Type:


  • Date:

    16 September 2022

  • Publication Status:

    In Press

  • Publisher

    Institute of Electrical and Electronics Engineers (IEEE)

  • DOI:


  • Cross Ref:


  • ISSN:


  • Funders:

    “Progetti di Ricerca” of Sapienza University of Rome


Comminiello, D., Nezamdoust, A., Scardapane, S., Scarpiniti, M., Hussain, A., & Uncini, A. (in press). A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling. IEEE Transactions on Systems Man and Cybernetics: Systems,



Efficient adaptive filtering, frequency-domain adaptive filters (FDAFs), functional links, low-complexity algorithms, nonlinear adaptive filters

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