ANN-based error reduction for experimentally modeled sensors

A method for correcting the effects of multiple error sources in differential transducers is proposed. The correction is carried out by a nonlinear multidimensional inverse model of the transducer based on an artificial neural network. The model exploits independent information provided by the diffe...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on instrumentation and measurement Vol. 51; no. 1; pp. 23 - 30
Main Authors Arpaia, P., Daponte, P., Grimaldi, D., Michaeli, L.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.02.2002
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Circuits
Electric potential
Error correction
Error reduction
Instrumentation
Intelligent sensors
Interference
Inverse problems
Mathematical models
Multidimensional systems
Nonlinearity
Proposals
Transducers
Voltage
Voltage control
Online AccessGet full text
ISSN0018-9456
1557-9662
DOI10.1109/19.989891

Cover

More Information
Summary:A method for correcting the effects of multiple error sources in differential transducers is proposed. The correction is carried out by a nonlinear multidimensional inverse model of the transducer based on an artificial neural network. The model exploits independent information provided by the difference in actual characteristics of the sensing elements, and by an easily controllable auxiliary quantity (e.g., supply voltage of conditioning circuit). Experimental results of the correction of an eddy-current displacement transducer subject to the combined interference of structural and geometrical parameters highlight the practical effectiveness of the proposed method.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
content type line 23
ISSN:0018-9456
1557-9662
DOI:10.1109/19.989891