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CALIBRATION OF A LOAD CELL USING A NEURAL NETWORK

dc.creatorVásquez Céspedes, Horacio
dc.date2011-07-19
dc.date.accessioned2016-05-03T15:10:13Z
dc.date.available2016-05-03T15:10:13Z
dc.identifierhttp://revistas.ucr.ac.cr/index.php/ingenieria/article/view/6438
dc.identifier10.15517/ring.v12i1-2.6438
dc.identifier.urihttps://hdl.handle.net/10669/24429
dc.descriptionA 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.es-ES
dc.formatapplication/pdf
dc.languagespa
dc.publisherUniversidad de Costa Ricaen-US
dc.rightsCopyright (c) 2014 Revista Ingenieríaen-US
dc.sourceJournal of Tropical Engineering; Vol 12, No 1-2 (2002)en-US
dc.sourceRevista Ingeniería; Vol 12, No 1-2 (2002)es-ES
dc.sourceIngeniería; Vol 12, No 1-2 (2002)pt-PT
dc.source2215-2652
dc.source1409-2441
dc.titleCALIBRATION OF A LOAD CELL USING A NEURAL NETWORKes-ES
dc.typeartículo original


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