论文标题
频域的研究指数函数链路网络过滤器
Study of Frequency domain exponential functional link network filters
论文作者
论文摘要
指数功能链路网络（EFLN）滤波器由于其增强的非线性建模能力而引起了极大的兴趣。但是，随着基于EFLN的过滤器的尺寸生长，计算复杂性将大大增加。为了提高计算效率，我们在本文中提出了一个新型的频域指数链接网络（FDEFLN）滤波器。这个想法是在扩展的输入数据的块中组织样本，从时域转换为频域，从而使用重叠式方法在频域中执行过滤和适应过程。还开发了基于FDEFLN的非线性活动噪声控制（NANC）系统，以形成频域指数过滤的最小均值（FDEFSLMS）算法。此外，分析了算法的稳定性，稳态性能和计算复杂性。最后，一些数值实验证实了在非线性系统识别，声学回声取消和NANC实现中提出的基于FDEFLN的算法，这表明了更好的计算效率。
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.
