@article{MAKHILLJEAS20061112544,
    title = {Calibration Using Artificial Neural Networks},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {1},
    number = {1},
    pages = {1-6},
    year = {2006},
    issn = {1816-949x},
    doi = {jeasci.2006.1.6},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2006.1.6},
    author = {H. Vasquez and},
    keywords = {Artificial neural networks,load cell calibration,wheatstone bridge,levenberg-marquardt optimization},
    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.}
    }