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The exponential functional link network (EFLN) filter has attractedtremendous interest due to its enhanced nonlinear modeling capability. However,the computational complexity will dramatically increase with the dimensiongrowth of the EFLN-based filter. To improve the computational efficiency, wepropose a novel frequency domain exponential functional link network (FDEFLN)filter in this paper. The idea is to organize the samples in blocks of expandedinput data, transform them from time domain to frequency domain, and thusexecute the filtering and adaptation procedures in frequency domain with theoverlap-save method. A FDEFLN-based nonlinear active noise control (NANC)system has also been developed to form the frequency domain exponentialfiltered-s least mean-square (FDEFsLMS) algorithm. Moreover, the stability,steady-state performance and computational complexity of algorithms areanalyzed. Finally, several numerical experiments corroborate the proposedFDEFLN-based algorithms in nonlinear system identification, acoustic echocancellation and NANC implementations, which demonstrate much bettercomputational efficiency.

Study of Frequency domain exponential functional link network filters