Chaotic signal denoising based on simplified convolutional denoising auto-encoder
Chaotic signal denoising based on simplified convolutional denoising auto-encoder
复制标题
基于简化卷积去噪自编码器的混沌信号去噪
DOI：
10.1016/j.chaos.2022.112333
复制
发表时间：
2022
影响因子：
7.8
通讯作者：
Shanxiang Lyu
中科院分区：
文献类型：
--
作者：
Shuting Lou;Jiarui Deng;Shanxiang Lyu
混沌是自然界中普遍存在的一种现象，但观测到的混沌信号往往被噪声所污染。在这项工作中，我们从深度学习的角度考虑混沌信号去噪，并提出了一种称为简化卷积去噪自动编码器（SCDAE）的混沌信号去噪方法。该方法包括一个编码器和一个解码器，共13层，并需要最少的预处理步骤。我们的仿真结果表明，该方法可以实现更小的均方根误差和更好的增殖指数比传统的去噪技术。·我们从深度学习的角度来构思混沌信号去噪任务，并提出了一种新的混沌信号去噪算法，称为简化卷积去噪自动编码器。该模型总共只有13层，比以前的深度学习模型简单得多。·与现有方法相比，该方法具有更高的信噪比和更小的均方根误差，并且保持了混沌信号的原始增殖指数。
Chaos is a ubiquitous phenomenon in nature, but the observed chaotic signals are often contaminated by noises. In this work, we consider chaotic signal denoising from the perspective of deep learning, and propose a chaotic signal denoising method referred to as Simplified Convolutional Denoising Auto-Encoder (SCDAE). The method consists of an encoder and a decoder with 13 layers in total, and requires minimal preprocessing steps. Our simulation results show that the proposed method can achieve smaller root mean square errors and better proliferation exponents than conventional denoising techniques. • We conceive the chaotic signal denoising task from the perspective of deep learning, and propose a novel chaotic signal denoising algorithm referred to as simplified convolutional denoising auto-encoder. • The model only has 13 layers in total, which is much simpler than previous deep learning models. • Compared to the state-of-the-arts, our method achieves larger SNR , smaller RMSE, and closely maintains the original proliferation exponent of the chaotic signal.