Wavelet-Based Neural Network Adaptive Filter for sEMG Denoising

Abstract:

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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.

Info:

Periodical:

Edited by:

Dongye Sun, Wen-Pei Sung and Ran Chen

Pages:

4259-4264

DOI:

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

Citation:

J. H. Wang et al., "Wavelet-Based Neural Network Adaptive Filter for sEMG Denoising", Applied Mechanics and Materials, Vols. 121-126, pp. 4259-4264, 2012

Online since:

October 2011

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Price:

$35.00

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