Parallel frequency function-deep neural network for efficient complex broadband signal approximation
This work addresses the computational inefficiency in neural network training for signal processing applications, offering an incremental improvement over existing methods like PhaseDNN.
The paper tackles the problem of efficiently approximating complex broadband signals with high-frequency components using neural networks, which suffer from spectral bias leading to slow training. The proposed Parallel Frequency Function-Deep Neural Network (PFF-DNN) method reduces computational overhead while maintaining accuracy, as verified by numerical experiments on six typical signals.
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.