并行频率函数-深度神经网络用于复杂宽带信号的高效逼近
Parallel frequency function-deep neural network for efficient complex broadband signal approximation
摘要：
神经网络本质上是一个通过调整网络权重进行特征拟合的高维复杂映射模型。然而，在网络训练中的频谱偏差导致难以忍受的训练时期，以适应宽带信号中的高频成分。为了提高高频分量的拟合效率，最近通过结合复频段提取和频移技术提出了PhaseDNN [Cai et al. SIAM J. SCI. Comput. 42，A3285（2020）]。我们的论文致力于一个替代的候选人拟合复杂的信号与高频分量。在这里，提出了一种并行频率函数-深度神经网络（PFF-DNN），通过利用宽带信号的快速傅立叶分析和神经网络的频谱偏置特性，在确保拟合精度的同时抑制计算开销。通过对6种典型宽带信号的数值实验，验证了PFF-DNN方法的有效性。
英文摘要：
A neural network is essentially a high-dimensional complex mapping model by adjusting network weights for feature fitting. However, the spectral bias in network training leads to unbearable training epochs for fitting the high-frequency components in broadband signals. To improve the fitting efficiency of high-frequency components, the PhaseDNN was proposed recently by combining complex frequency band extraction and frequency shift techniques [Cai et al. SIAM J. SCI. COMPUT. 42, A3285 (2020)]. Our paper is devoted to an alternative candidate for fitting complex signals with high-frequency components. Here, a parallel frequency function-deep neural network (PFF-DNN) is proposed to suppress computational overhead while ensuring fitting accuracy by utilizing fast Fourier analysis of broadband signals and the spectral bias nature of neural networks. The effectiveness and efficiency of the proposed PFF-DNN method are verified based on detailed numerical experiments for six typical broadband signals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
