Modeling and Classification of sEMG Based on Blind Identification Theory

被引：0
|
作者：
Li, Yang [1 ]
Tian, Yantao [1 ]
Shang, Xiaojing [1 ]
Chen, Wanzhong [1 ]
机构：
[1] Jilin Univ, Sch Commun Engn, Changchun 130025, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2011, PT III | 2011年 / 6677卷
关键词：
sEMG; Blind Identification; Hammerstein-Wiener Model;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Surface electromyography signal is non-stationary, susceptible to external interference. For this situation under this case, cyclostationary input with the inverse nonlinear mapping of the Hammerstein-Wiener model were combined to build surface electromyography model and to realize the blind discrete nonlinear system identification. The parameters of model were used as input of improved BP neural network. The experiments results demonstrated the effectiveness of this approach.
引用
收藏
页码：340 / 347
页数：8
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