Title :
ANN-based error reduction for experimentally modeled sensors
Author :
Arpaia, Pasquale ; Daponte, P. ; Grimaldi, Domenico ; Michaeli, Linus
Author_Institution :
Dipt. di Ingegneria Elettrica, Naples Univ., Italy
Abstract :
A method for correcting the effects of multiple error sources in differential transducers is proposed. The difference in actual characteristics of the sensing elements of the differential scheme, and an easily controllable auxiliary quantity (e.g. supply voltage of conditioning circuit) provide independent information for the correction. This is carried out by a nonlinear multidimensional inverse model of the transducer based on an artificial neural network. Experimental results of the correction of a variable-reluctance displacement transducer, subject to the combined interference of structural and geometrical parameters, highlight the effectiveness of the proposed method
Keywords :
displacement measurement; electric sensing devices; error compensation; magnetic sensors; measurement errors; modelling; multilayer perceptrons; radial basis function networks; redundancy; transducers; ANN-based error reduction; MLP; RBF; combined interference; compensation; conditioning circuit; controllable auxiliary quantity; differential transducers; experimentally modeled sensors; geometrical parameters; multiple error sources; nonlinear multidimensional inverse model; redundancy; structural parameters; supply voltage; systematic error; variable-reluctance displacement transducer; Artificial neural networks; Displacement control; Error correction; Inverse problems; Neural networks; Proposals; Sensor phenomena and characterization; Solid modeling; Telecommunications; Transducers;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 2000. IMTC 2000. Proceedings of the 17th IEEE
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-5890-2
DOI :
10.1109/IMTC.2000.848721
