FIELD: physics.
SUBSTANCE: present invention relates to automatic or automated calibration of systems and sensors by "learning" an intelligent sensor during calibration. Intelligent sensors are calibrated by feeding a set of known signals to said input, observing a plurality of output signals and using an artificial neural network for approximation (interpolation) of a calibrated sensor mathematical model which must satisfy certain conditions in accordance with a commutative diagram corresponding to the isomorphism principle. By training an artificial neural network, an inverse (or direct) model of the calibrated intelligent sensor is implicitly generated, allowing with a given accuracy to recover the known set of signals acting on the input of the system (or directly observed at its output).
EFFECT: technical result is simple procedures for designing sensors based on use of the new technology of individual calibration and training disclosed in the invention, as a result of which an "intelligent" sensor can be obtained, which provides for recovery of the physical quantity acting thereon with a given accuracy in the entire range of operating conditions, and unique for sensor of specific type selective increase of sensitivity in range of change of measured physical quantity.
1 cl, 3 dwg
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Authors
Dates
2020-08-21—Published
2019-06-10—Filed