H. Vasquez  , D.J. Fonseca  , 
    Calibration Using Artificial Neural Networks,
    Journal of Engineering and Applied Sciences,
    Volume 1,Issue 1,
    2006,
    Pages 1-6,
    ISSN 1816-949x,
    jeasci.2006.1.6,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2006.1.6)
    Abstract: This study discusses the design and development of an Artificial Neural network (ANN) model to monitor the force applied to a strain-gage load cell.  The reference voltage applied to a Wheatstone bridge formed by the strain gages, the amplification of the Wheatstone bridge’s output voltage and the digitized value of the amplifier’s output voltage acquired by a microprocessor represented the input to the ANN model.  The output of the ANN was defined as the estimated value of the load acting on the load cell.  In this study, a 5-3-1 neural network architecture proved to yield the best results, being the backpropagation Levenberg-Marquardt optimization algorithm the selected training paradigm.  Based on the results obtained, it was concluded that neural networks offer a good option to calibrate an instrument, equipment, or system that operates under variable input conditions.
    Keywords: Artificial neural networks;load cell calibration;wheatstone bridge;levenberg-marquardt optimization