CALIBRATION OF A LOAD CELL USING A NEURAL NETWORK

A neural network is used to calibrate a load cell that was built using strain gages. The inputs to the neural networkare the reference voltage applied to the Wheatstone bridge formed by the strain gages, the amplification value appliedto the Wheatstone bridge's output voltage, and the 8-bit dig...

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Main Author: Vásquez Céspedes, Horacio
Format: artículo científico
Language: Español
Published: Universidad de Costa Rica 2011
Online Access: http://revistas.ucr.ac.cr/index.php/ingenieria/article/view/6438
http://hdl.handle.net/10669/24429
Summary: A neural network is used to calibrate a load cell that was built using strain gages. The inputs to the neural networkare the reference voltage applied to the Wheatstone bridge formed by the strain gages, the amplification value appliedto the Wheatstone bridge's output voltage, and the 8-bit digitized voltage value acquired by a microprocessor. Theoutput of the network is the estimated value of the weight being applied to the load cell. The network's main objectivewas to learn an accurate input-output relationship of the variables in the load cell system. The backpropagationLevenberg-Marquardt algorithm was used to train the network, and satisfactory results were obtained with a 5-3-1neural network. This project could be used as an example to design similar neural networks for other applications.