Recurrent Dynamic Neural Network Model for Myoelectric-based Control of a Prosthetic Hand

被引：0

作者：

Teban, Teodor-Adrian ^{[1
]}

Precup, Radu-Emil ^{[1
]}

Voisan, Emil-Ioan ^{[1
]}

Alves de Oliveira, Thiago Eustaquio ^{[2
]}

Petriu, Emil M. ^{[2
]}

机构：

[1] Politehn Univ Timisoara, Dept Automat & Appl Informat, Timisoara, Romania

[2] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON, Canada

来源：

2016 ANNUAL IEEE SYSTEMS CONFERENCE (SYSCON) | 2016年

关键词：

myoelectric sensors; NARX recurrent neural networks; prosthetic hand; CONTROL-SYSTEM; FUZZY; ALGORITHM; INTERVAL; SIGNALS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper discusses a novel nonlinear autoregressive network with exogenous inputs (NARX) neural network (NN) model of the nonlinear dynamic mechanisms occurring in the myoelectric-based control of prosthetic hand fingers. The experimental results demonstrate the good performance of the NN architecture in terms of output response and mean squared error. The comparison of experimental results also shows that the proposed NARX recurrent NN outperforms a linear recurrent NN with the same training algorithm.

引用

页码：117 / 122

页数：6

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