基于表面肌电信号的自适应模糊神经网络肘关节意图识别

Yongbai Liu, Chunxu Li, Zhaowei Teng, Keping Liu, Gang-Yi Wang, Zhongbo Sun
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引用次数: 4

摘要

本文建立了基于表面肌电图(sEMG)的自适应模糊神经网络(AFNN)估算肘关节角度,并从快速和准确的角度进行了研究。此外,将经典的数据预测方法——反向传播神经网络(BPNN)和径向基函数人工神经网络(RBFNN)应用于肘关节角度的估计,并与AFNN进行比较。最后,实验仿真和结果分析表明,AFNN的速度和精度都优于BPNN和RBFNN。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intention Recognition of Elbow Joint based on sEMG Using Adaptive Fuzzy Neural Network
In this paper, the adaptive fuzzy neural network (AFNN) based on the surface electromyography (sEMG) for estimating the elbow joint angle is established and investigated from the perspective of rapidity and accuracy. In addition, back propagation neural network (BPNN) and artificial neural network of radial basis function (RBFNN), as the classical method for data forecasting, have been applied to estimate the elbow joint angle for comparing with AFNN. Ultimately, the experimental simulation and result analysis demonstrate that the rapidity and accuracy of AFNN is superior to BPNN and RBFNN.
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