A learning algorithm for self-calibration of a voltage calibrator

An algorithm either to extend the calibration period or to reduce the measurement uncertainty of a DC voltage reference module is presented. This module is used either as a transfer, independent, or working standard, or as a reference module incorporated into a larger measuring system. The basic ide...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 1992-12, Vol.41 (6), p.991-996
Main Authors: Drnovsek, J., Fefer, D., Jeglic, A.
Format: Article
Language:English
Subjects:
Applied sciences
Calibration
Collaborative work
Electronics
Exact sciences and technology
History
Instruments
Measurement standards
Measurement uncertainty
Neural networks
Particle measurements
Random processes
Testing, measurement, noise and reliability
Voltage
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Summary:An algorithm either to extend the calibration period or to reduce the measurement uncertainty of a DC voltage reference module is presented. This module is used either as a transfer, independent, or working standard, or as a reference module incorporated into a larger measuring system. The basic idea is that the deviation history of measured voltage differences of reference elements of a group reference module during the calibration period can be used as a learning period for a neural network. This neural network, when created, can numerically correct particular reference elements later in the working period. Results were obtained by simulation and evaluated on the basis of empirical data and simulated input functions. Hardware solutions to model this algorithm are discussed.< >
ISSN:0018-9456
1557-9662
DOI:10.1109/19.199379