Study of Frequency domain exponential functional link network filters
This work addresses computational bottlenecks in nonlinear filtering for applications like noise control and echo cancellation, but it is incremental as it builds on existing EFLN methods.
The authors tackled the high computational complexity of exponential functional link network (EFLN) filters by proposing a frequency domain version (FDEFLN) that processes data in blocks using the overlap-save method, resulting in significantly improved computational efficiency as demonstrated in numerical experiments.
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.