Nonlinear Static Decoupling of Six-Dimension Force Sensor for Walker Dynamometer System Based on Artificial Neural Network

被引：8
|
作者：
Ming, Dong [1 ]
Zhang, Xi [1 ]
Liu, Xiuyun [1 ]
Wan, Baikun [1 ]
Hu, Yong [2 ]
Luk, K. D. K. [2 ]
机构：
[1] Tianjin Univ, Dept Biomed Engn, Tianjin 300072, Peoples R China
[2] Univ Hong Kong, Dept Orthopaed & Trumat, Hong Hom, Hong Kong, Peoples R China
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MEASUREMENT SYSTEMS AND APPLICATIONS | 2009年
基金：
中国国家自然科学基金;
关键词：
static coupling; walker; Back Propagation neural network; Radial Basis Function neural network;
D O I：
10.1109/CIMSA.2009.5069909
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The static coupling of six-dimension force sensor for walker dynamometer system is a key factor to limit its measuring precision. A new decoupling method based on artificial neural network is proposed in this paper. Relevant error check results shows that, after the calibration by using the Back Propagation neural network and Radial Basis Function neural networks, the maximal system precision error with single-direction force was 7.78% and 4.33% and the maximal crosstalk was 7.49% and 6.52%,respectively. In comparison with traditional linear calibration method, the proposed technique can effectively increase the measurement accuracy of walker loads and greatly decrease the coupling effect.
引用
收藏
页码：14 / +
页数：2
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