Data Fusion based on RBF Neural Network for Error Compensation in Resistance Strain Gauge Force Transducers

Author

Chen, Yan ; Boulet, Benoit ; Chen, Ping ; Zhao, Mingbo

Author_Institution

McGill Univ., Montreal

Volume

3

fYear

2007

fDate

July 30 2007-Aug. 1 2007

Firstpage

86

Lastpage

91

Abstract

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 -10deg C to 60degC 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.

Keywords

computerised instrumentation; force measurement; force sensors; radial basis function networks; sensor fusion; transducers; RBF neural network; data fusion; error compensation; multiquadrics kernel; radial basis function neural network; resistance strain gauge force transducers; vehicle traction force measurements; Capacitive sensors; Elasticity; Error compensation; Force measurement; Kernel; Neural networks; Strain measurement; Temperature; Transducers; Vehicles; Data fusion; Error compensation; Multi-quadrics kernel; RBF neural network; Traction force;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on

Conference_Location

Qingdao

Print_ISBN

978-0-7695-2909-7

Type

conf

DOI

10.1109/SNPD.2007.503

Filename

4287829