  The exponential functional link network (EFLN) filter has attracted
tremendous interest due to its enhanced nonlinear modeling capability. However,
the computational complexity will dramatically increase with the dimension
growth of the EFLN-based filter. To improve the computational efficiency, we
propose a novel frequency domain exponential functional link network (FDEFLN)
filter in this paper. The idea is to organize the samples in blocks of expanded
input data, transform them from time domain to frequency domain, and thus
execute the filtering and adaptation procedures in frequency domain with the
overlap-save method. A FDEFLN-based nonlinear active noise control (NANC)
system has also been developed to form the frequency domain exponential
filtered-s least mean-square (FDEFsLMS) algorithm. Moreover, the stability,
steady-state performance and computational complexity of algorithms are
analyzed. Finally, several numerical experiments corroborate the proposed
FDEFLN-based algorithms in nonlinear system identification, acoustic echo
cancellation and NANC implementations, which demonstrate much better
computational efficiency.
