Systematic error correction for experimentally modeled sensors by using ANNs

Author

Arpaia, Pasquale ; Daponte, Pasquale ; Grimaldi, Domenico ; Michaeli, Linus

Author_Institution

Dipt. di Ingegneria Elettrica, Naples Univ., Italy

Volume

3

fYear

1999

fDate

1999

Firstpage

1635

Abstract

The paper deals with the compensation of the systematic uncertainty of sensors subject to nonlinear and combined influence parameters. The compensation is based on a second sensor and a digital artificial neural network (ANN). This heuristic fully a-posteriori approach allows the twofold problems of (i) the complex mathematical modeling of the influence on the measurement, and (ii) the effective solution of the nonlinear model to be simultaneously bypassed. Experimental results of the characterization of a variable-reluctance proximity transducer highlight the effectiveness of the proposed compensation scheme

Keywords

computerised instrumentation; error compensation; error correction; intelligent sensors; measurement errors; modelling; neural nets; signal processing equipment; characterization; compensation scheme; complex mathematical modeling; digital ANN; digital artificial neural network; experimentally modeled sensors; nonlinear model; systematic error correction; systematic uncertainty; variable-reluctance proximity transducer; Artificial neural networks; Calibration; Error compensation; Error correction; Inverse problems; Mathematical model; Sensor phenomena and characterization; Sensor systems; Transducers; World Wide Web;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 1999. IMTC/99. Proceedings of the 16th IEEE

Conference_Location

Venice

ISSN

1091-5281

Print_ISBN

0-7803-5276-9

Type

conf

DOI

10.1109/IMTC.1999.776101

Filename

776101