Experimental ANN-based modeling of an adjustable damper

Juan C. Tudon-Martinez, Ruben Morales-Menendez, Ricardo Ramirez-Mendoza, Luis Garza-Castanon

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

© 2014 IEEE. A model for a Magneto-Rheological (MR) damper based on Artificial Neural Networks (ANN) is proposed. The design of the ANN model is focused to get the best architecture that manages the trade-off between computing cost and performance. Experimental data provided from two commercial MR dampers with different properties have been used to validate the performance of the proposed ANN model in comparison with the classical parametric model of Bingham. Based on the Root Mean Square Error index, an average error of 7.2 % is obtained by the ANN model, by taking into account 5 experiments with 10 replicas each one; while the Bingham model has 13.8 % of error.
Original languageEnglish
Pages2512-2518
Number of pages7
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
EventProceedings of the International Joint Conference on Neural Networks -
Duration: 1 Jan 2014 → …

Conference

ConferenceProceedings of the International Joint Conference on Neural Networks
Period1/1/14 → …

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Neural networks
Mean square error
Costs
Experiments

Cite this

Tudon-Martinez, J. C., Morales-Menendez, R., Ramirez-Mendoza, R., & Garza-Castanon, L. (2014). Experimental ANN-based modeling of an adjustable damper. 2512-2518. Paper presented at Proceedings of the International Joint Conference on Neural Networks, . https://doi.org/10.1109/IJCNN.2014.6889391
Tudon-Martinez, Juan C. ; Morales-Menendez, Ruben ; Ramirez-Mendoza, Ricardo ; Garza-Castanon, Luis. / Experimental ANN-based modeling of an adjustable damper. Paper presented at Proceedings of the International Joint Conference on Neural Networks, .7 p.
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abstract = "{\circledC} 2014 IEEE. A model for a Magneto-Rheological (MR) damper based on Artificial Neural Networks (ANN) is proposed. The design of the ANN model is focused to get the best architecture that manages the trade-off between computing cost and performance. Experimental data provided from two commercial MR dampers with different properties have been used to validate the performance of the proposed ANN model in comparison with the classical parametric model of Bingham. Based on the Root Mean Square Error index, an average error of 7.2 {\%} is obtained by the ANN model, by taking into account 5 experiments with 10 replicas each one; while the Bingham model has 13.8 {\%} of error.",
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Tudon-Martinez, JC, Morales-Menendez, R, Ramirez-Mendoza, R & Garza-Castanon, L 2014, 'Experimental ANN-based modeling of an adjustable damper' Paper presented at Proceedings of the International Joint Conference on Neural Networks, 1/1/14, pp. 2512-2518. https://doi.org/10.1109/IJCNN.2014.6889391

Experimental ANN-based modeling of an adjustable damper. / Tudon-Martinez, Juan C.; Morales-Menendez, Ruben; Ramirez-Mendoza, Ricardo; Garza-Castanon, Luis.

2014. 2512-2518 Paper presented at Proceedings of the International Joint Conference on Neural Networks, .

Research output: Contribution to conferencePaper

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AB - © 2014 IEEE. A model for a Magneto-Rheological (MR) damper based on Artificial Neural Networks (ANN) is proposed. The design of the ANN model is focused to get the best architecture that manages the trade-off between computing cost and performance. Experimental data provided from two commercial MR dampers with different properties have been used to validate the performance of the proposed ANN model in comparison with the classical parametric model of Bingham. Based on the Root Mean Square Error index, an average error of 7.2 % is obtained by the ANN model, by taking into account 5 experiments with 10 replicas each one; while the Bingham model has 13.8 % of error.

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Tudon-Martinez JC, Morales-Menendez R, Ramirez-Mendoza R, Garza-Castanon L. Experimental ANN-based modeling of an adjustable damper. 2014. Paper presented at Proceedings of the International Joint Conference on Neural Networks, . https://doi.org/10.1109/IJCNN.2014.6889391