论文标题
非平稳信号的可训练小波神经网络
Trainable Wavelet Neural Network for Non-Stationary Signals
论文作者
论文摘要
这项工作介绍了一个小波神经网络，以学习适合非平稳信号的滤网，并提高数字信号处理的解释性和性能。该网络使用小波变换作为神经网络的第一层，其中卷积是复杂的Morlet小波的参数化函数。在简化的数据和大气重力波上，实验结果表明，网络很快收敛，在嘈杂的数据上很好地概括，并且胜过标准网络体系结构。
This work introduces a wavelet neural network to learn a filter-bank specialized to fit non-stationary signals and improve interpretability and performance for digital signal processing. The network uses a wavelet transform as the first layer of a neural network where the convolution is a parameterized function of the complex Morlet wavelet. Experimental results, on both simplified data and atmospheric gravity waves, show the network is quick to converge, generalizes well on noisy data, and outperforms standard network architectures.
