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.
翻译:指数性功能链接网络过滤器因其增强的非线性建模能力而引起了极大的兴趣,然而,随着以ELFLN为基础的过滤器的尺寸增长,计算复杂性将急剧增加。为了提高计算效率,我们提议在本文中采用一个新的频率域指数性功能链接网络过滤器(FDEFLN),目的是将样本组织成扩大输入数据区块,将其从时域转换为频率域,从而在频域内执行过滤和适应程序,同时采用重叠保存方法。基于FDEFLN的非线性活性噪音控制系统(NANC)也已经发展成频率域指数性过滤最小平均方程式(FDEFLMS)算法。此外,还分析了算法的稳定性、稳定性性能和计算复杂性。最后,若干数字实验证实了拟议的非线性系统识别、声回声取消和NANC的计算效率要好得多的基于FDEFLN的算法。