  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. In this paper, we propose a new efficient nonlinear
model for online applications. The proposed algorithm is based on the
linear-in-the-parameters (LIP) nonlinear filters and their implementation as
functional link adaptive filters (FLAFs). We focus here on a new effective and
efficient approach for FLAFs based on frequency-domain adaptive filters. We
introduce the class of frequency-domain functional link adaptive filters
(FD-FLAFs) and propose a partitioned block approach for their implementation.
We also investigate on the functional link expansions that provide the most
significant benefits operating with limited resources in the frequency-domain.
We present and compare FD-FLAFs with different expansions to identify the LIP
nonlinear filters showing the best tradeoff between performance and
computational complexity. Experimental results prove that the frequency domain
LIP nonlinear filters can be considered as an efficient and effective solution
for online applications, like the nonlinear acoustic echo cancellation.
