Experimental ANN-based modeling of an adjustable damper

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

doi:10.1109/IJCNN.2014.6889391

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 conference › Paper

3 Citations (Scopus)

Abstract

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 language	English
Pages	2512-2518
Number of pages	7
DOIs	https://doi.org/10.1109/IJCNN.2014.6889391
Publication status	Published - 3 Sept 2014
Externally published	Yes
Event	Proceedings of the International Joint Conference on Neural Networks - Duration: 18 Nov 2009 → …

Conference

Conference	Proceedings of the International Joint Conference on Neural Networks
Period	18/11/09 → …

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

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10.1109/IJCNN.2014.6889391

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84908479892&origin=inward

Cite this

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abstract = "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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Experimental ANN-based modeling of an adjustable damper

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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