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

3 Citations (Scopus)


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
Number of pages7
Publication statusPublished - 3 Sept 2014
Externally publishedYes
EventProceedings of the International Joint Conference on Neural Networks -
Duration: 18 Nov 2009 → …


ConferenceProceedings of the International Joint Conference on Neural Networks
Period18/11/09 → …

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

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

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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