Experimental ANN-based modeling of an adjustable damper

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

Resultado de la investigación

1 Cita (Scopus)

Resumen

© 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.
Idioma originalEnglish
Páginas2512-2518
Número de páginas7
DOI
EstadoPublished - 1 ene 2014
Publicado de forma externa
EventoProceedings of the International Joint Conference on Neural Networks -
Duración: 1 ene 2014 → …

Conference

ConferenceProceedings of the International Joint Conference on Neural Networks
Período1/1/14 → …

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  • Citar esto

    Tudon-Martinez, J. C., Morales-Menendez, R., Ramirez-Mendoza, R., & Garza-Castanon, L. (2014). Experimental ANN-based modeling of an adjustable damper. 2512-2518. Papel presentado en Proceedings of the International Joint Conference on Neural Networks, . https://doi.org/10.1109/IJCNN.2014.6889391