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Chaotic signal denoising based on simplified convolutional denoising auto-encoder

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  • Lou, Shuting
  • Deng, Jiarui
  • Lyu, Shanxiang

Abstract

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.

Suggested Citation

  • Lou, Shuting & Deng, Jiarui & Lyu, Shanxiang, 2022. "Chaotic signal denoising based on simplified convolutional denoising auto-encoder," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:chsofr:v:161:y:2022:i:c:s0960077922005434
    DOI: 10.1016/j.chaos.2022.112333
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    References listed on IDEAS

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    1. Shang, Li-Jen & Shyu, Kuo-Kai, 2009. "A method for extracting chaotic signal from noisy environment," Chaos, Solitons & Fractals, Elsevier, vol. 42(2), pages 1120-1125.
    2. Huang, Pengfei & Chai, Yi & Chen, Xiaolong, 2022. "Multiple dynamics analysis of Lorenz-family systems and the application in signal detection," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    3. Li, Yin & Li, Fagang & Lyu, Shanxiang & Xu, Meng & Wang, Shiyuan, 2021. "Blind extraction of ECG signals based on similarity in the phase space," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
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    Cited by:

    1. Ren, Jinfu & Liu, Yang & Liu, Jiming, 2023. "Chaotic behavior learning via information tracking," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. Deng, Jiarui & Lao, Huimin & Lyu, Shanxiang, 2023. "Compressed chaotic signal reconstruction based on deep learning," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).

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