loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)
Data Fusion based on RBF Neural Network for Error Compensation in Resistance Strain Gauge Force Transducers
Haier International Training Center, Qingdao, China
July 30-August 01
ISBN: 0-7695-2909-7
ASCII Text
x 
 
Yan Chen, Benoit Boulet, Ping Chen, Mingbo Zhao, "Data Fusion based on RBF Neural Network for Error Compensation in Resistance Strain Gauge Force Transducers," Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on, vol. 3, pp. 86-91, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007.
 
 
BibTex
x
 
@article{ 10.1109/SNPD.2007.503,
author = {Yan Chen and Benoit Boulet and Ping Chen and Mingbo Zhao},
title = {Data Fusion based on RBF Neural Network for Error Compensation in Resistance Strain Gauge Force Transducers},
journal ={Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on},
volume = {3},
year = {2007},
isbn = {0-7695-2909-7},
pages = {86-91},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.503},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
TI - Data Fusion based on RBF Neural Network for Error Compensation in Resistance Strain Gauge Force Transducers
SN - 0-7695-2909-7
SP86
EP91
A1 - Yan Chen,
A1 - Benoit Boulet,
A1 - Ping Chen,
A1 - Mingbo Zhao,
PY - 2007
KW - Data fusion; RBF neural network; Multi-quadrics kernel; Error compensation; Traction force
VL - 3
JA - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
ER -
 
Yan Chen, McGill University, Canada
Benoit Boulet, McGill University, Canada
Ping Chen, Shandong University of Technology, China
Mingbo Zhao, Shandong University of Technology, China
Many factors, such as environmental temperature and material elasticity, can affect the output of resistance strain gauge force transducers used in vehicle traction force measurements. A data fusion method based on radial basis function (RBF) neural network is proposed to reduce the negative effects and compensate the measurement error. A multiquadrics kernel is utilized as the kernel function for the RBF neural networks. It fuses the environmental temperature in the force measurement while realizing an accurate compensation of errors. Tests have been carried out within temperature ranging from -10 ..C to 60 ..C and the results show that the maximum error with load 80000N is below 0.5% after compensation while it is greater than 6% before compensation.
Index Terms:
Data fusion; RBF neural network; Multi-quadrics kernel; Error compensation; Traction force
Citation:
Yan Chen, Benoit Boulet, Ping Chen, Mingbo Zhao, "Data Fusion based on RBF Neural Network for Error Compensation in Resistance Strain Gauge Force Transducers," snpd, vol. 3, pp.86-91, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007
Usage of this product signifies your acceptance of the Terms of Use.