Wavelet-Based Neural Network Adaptive Filter for sEMG Denoising

Jian Hui Wang; Na Chen; Qian Xiao; Jian You Xu; Shu Sheng Gu

doi:10.4028/www.scientific.net/AMM.121-126.4259

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 121-126Wavelet-Based Neural Network Adaptive Filter for...

Wavelet-Based Neural Network Adaptive Filter for sEMG Denoising

Abstract:

For the large computation workload of the adaptive filter algorithm and the low filtering speed of the adaptive filter model based on wavelet transform, a wavelet-based neural network adaptive filter model is constructed in this paper. As the neural network has the capacity of distributed storage and fast self-evolution, Hopfield neural network is used to implement adaptive filtering algorithm LMS, so as to increase the computing speed. The model applied to sEMG signal denoising can achieve a better filtering effect.

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Periodical:

Applied Mechanics and Materials (Volumes 121-126)

Pages:

4259-4264

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.121-126.4259

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Online since:

October 2011

Authors:

Jian Hui Wang, Na Chen, Qian Xiao, Jian You Xu, Shu Sheng Gu

Keywords:

Adaptive Filter, De-Noising, Discrete Wavelet Transform (DWT), Hopfield Neural Network, SEMG

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