Intention Recognition of Elbow Joint based on sEMG Using Adaptive Fuzzy Neural Network

被引：2

作者：

Liu, Yongbai ^{[1
]}

Teng, Zhaowei ^{[1
]}

Wang, Gang ^{[1
]}

Li, Chunxu ^{[2
]}

Liu, Keping ^{[1
]}

Sun, Zhongbo ^{[1
]}

机构：

[1] Changchun Univ Technol, Dept Control Engn, Changchun, Peoples R China

[2] Univ Plymouth, Ctr Robot & Neural Syst, Plymouth, Devon, England

来源：

2020 5TH INTERNATIONAL CONFERENCE ON MECHANICAL, CONTROL AND COMPUTER ENGINEERING (ICMCCE 2020) | 2020年

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Adaptive fuzzy neural network; sEMG signal; RMS error; Intention recognition; Rehabilitation; EXOSKELETON; ASSISTANCE; DESIGN;

D O I：

10.1109/ICMCCE51767.2020.00240

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1087 / 1092

页数：6

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