  Nonlinear models are known to provide excellent performance in real-world
applications that often operate in non-ideal 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 (LIP) 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, whose implementation is
particularly suitable for online nonlinear modeling problems. We assess and
compare frequency-domain 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.
