Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
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.
How to cite this article:
H. Vasquez and D.J. Fonseca . Calibration Using Artificial Neural Networks.
DOI: https://doi.org/10.36478/jeasci.2006.1.6
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2006.1.6