一种新的自适应滤波结构:NLMS、DST-LMS和DCT-LMS方案在肌电信号建模中的比较研究

A. Veiga, Y. Iano, G. Carrijo
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引用次数: 3

摘要

本文的目标是提出一种具有轴向旋转LMS自适应滤波器的离散正弦变换变换域算法。基于最小均方变换(LMS)方程和轴向旋转离散正弦变换(DSTr)方程,推导了一种算法。通过计算机仿真,比较了其与归一化LMS (NLMS)、离散正弦变换LMS (DST-LMS)和离散余弦变换LMS (DCT-LMS)方案的性能。本文的另一个目标是使用DSTr-LMS算法研究肌电图(EMG)信号建模。这是一种可以用四阶自回归模型表示的信号。使用适当阶数的自适应滤波器作为预测器,其系数可以看作是该信号的表示。与其他算法的学习曲线相比,DSTr-LMS的学习曲线具有更好的收敛速度和等效的稳态均方误差(MSE)。因此,作者得出结论,这组系数是这类信号的一个很好的表示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new adaptive filter structure: comparative study of NLMS, DST-LMS and DCT-LMS schemes applied to electromyographic signal modelling
The goal of this work is to present a transform domain algorithm called discrete sine transform with axis rotation LMS adaptive filter. Based on the least-mean-square (LMS) and discrete sine transform with axis rotation (DSTr) equations, a proposed algorithm is deduced. The authors compare its performance, through computer simulations, with normalized LMS (NLMS), discrete sine transform LMS (DST-LMS) and discrete cosine transform LMS (DCT-LMS) schemes. Another goal of this paper is the study of electromyographic (EMG) signal modeling using the DSTr-LMS algorithm. This is a type of signal that can be represented by an autoregressive model of the fourth order. Using an adaptive filter with adequate order as a predictor, its coefficients can be viewed as a representation of this signal. The learning curves of DSTr-LMS exhibit a better convergence rate and equivalent values of steady state mean-square error (MSE) if compared with learning curves of the other algorithms already mentioned. Therefore, the authors conclude that this set of coefficients is a good representation of this type of signal.
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