Adaptive Noise Reduction Algorithm for Chaotic Signals Based on Wavelet Packet Transform

LIU Yunxia; BEI Guangxia; JIANG Zhongyun; MENG Qiang; SHI Huizhe

doi:10.11999/JEIT221137

Volume 45 Issue 10

Oct. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(10): 3676-3684

LIU Yunxia, BEI Guangxia, JIANG Zhongyun, MENG Qiang, SHI Huizhe. Adaptive Noise Reduction Algorithm for Chaotic Signals Based on Wavelet Packet Transform[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3676-3684. doi: 10.11999/JEIT221137

Citation:

LIU Yunxia, BEI Guangxia, JIANG Zhongyun, MENG Qiang, SHI Huizhe. Adaptive Noise Reduction Algorithm for Chaotic Signals Based on Wavelet Packet Transform[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3676-3684. doi: 10.11999/JEIT221137

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Adaptive Noise Reduction Algorithm for Chaotic Signals Based on Wavelet Packet Transform

doi: 10.11999/JEIT221137

Engineering Training Center, Shandong University of Science and Technology, Qingdao 266590, China

Funds: The National Metalworking and Engineering Training Young Teachers' Teaching Method Innovation Research Project (2022JJGX-WKJY-40), The 2022 Online Course Construction Project of Shandong University of Science and Technology (ZXK202242),The 2022 Education and Teaching Research “Stars Program” Project of Shandong University of Science and Technology (QX2022M91)

Received Date: 2022-08-30
Rev Recd Date: 2022-11-27

Available Online: 2022-11-30

Publish Date: 2023-10-31

Abstract

Abstract

To reflect better the inherent characteristics of chaotic systems, an adaptive noise reduction algorithm for chaotic signals based on wavelet packet transform is proposed. Firstly, the best decomposition level is determined according to the different correlation of wavelet packet coefficients in different decomposition scales, while the optimal wavelet packet basis is obtained with the logarithmic energy entropy as the cost function. Then, the approximate coefficients are projected in the local neighborhood and the detail coefficients are adaptively selected with the gradient descent algorithm in neural network. By minimizing the loss function, the influence of noises on chaotic signals is reduced to the greatest extent. Finally, simulations on the state variables originating from Rossler chaotic model verify the denoising superiority of the proposed algorithm for the chaotic signals.
- Wavelet packet,
- Local projection,
- Neural network,
- Chaos,
- Noise reduction

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References(21)

References

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