Open AccessARTIFICIAL NEURAL NETWORK BASED ONLINE SENSOR CALIBRATION AND COMPENSATION
Author(s) -
Shakeb A. Khan,
Tarikul Islam,
Gulshan Husain
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.6.3.454
Subject(s) - linearization , calibration , computer science , artificial neural network , compensation (psychology) , inverse , flexibility (engineering) , matlab , data acquisition , software , nonlinear system , convolutional neural network , artificial intelligence , data mining , control engineering , machine learning , engineering , mathematics , statistics , geometry , psychology , physics , quantum mechanics , psychoanalysis , programming language , operating system
This paper presents an artificial neural network (ANN) based generalized online method for sensor response linearization and calibration. Inverse modeling technique is used for sensor response linearization. Multilayer ANN is used for inverse modeling of sensor. The inverse model based technique automatically compensates the associated nonlinearity and estimates the measurand. The scheme is coded in MATLAB® for offline training and for online measurement and successfully implemented using NI PCI-6221 Data Acquisition (DAQ) card and LabVIEW® software. Manufacturing tolerances, environmental effects, and performance drifts due to aging bring up a need for frequent calibration, this ANN based inverse modeling technique provides greater flexibility and accuracy under such conditions.