Scaled conjugate gradient ANN for industrial sensors calibration

Autor: Karam M. Z. Othman, Abdulkreem M. Salih
Rok vydání: 2021
Předmět:
Popis: In this paper, artificial neural network is used to calibrate sensors that are commonly used in industry. Usually, such sensors have nonlinear input output characteristic that makes their calibration process rather inaccurate and unsatisfied. Artificial neural network is utilized in an inverse model learning mode to precisely calibrate such sensors. The scaled conjugate gradient (SCG) algorithm is used in the learning process. Three types of industrial sensors which are gas concentration sensor, force sensors and humidity sensors are considered in this work. It is found that the proposed calibration technique gives fast, robust and satisfactory results.
Databáze: OpenAIRE