Identification of Compliant Contact Force Parameters in Multibody Systems Based on the Neural Network Approach Related to Municipal Property Damages
American Journal of Neural Networks and Applications
Volume 3, Issue 5, October 2017, Pages: 49-55
Received: Nov. 14, 2017;
Accepted: Dec. 14, 2017;
Published: Jan. 8, 2018
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Gholamhossein Lari, Safety and Fire Fighting Organization, Shiraz Municipality, Shiraz, Iran
Saeed Ebrahimi, Department of Mechanical Engineering, Yazd University, Yazd, Iran
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In this paper, a new approach for identification of the compliant contact parameters model in multibody systems simulation using a neural network algorithm is presented. Based on the training and testing the network for some input and output data sets, a general framework is established for identification of these parameters. For this purpose, first, the literature devoted to the identification of contact parameters using analytical approaches and methods based on the neural network is reviewed in brief. Next, the proposed approach is outlined. Finally, considering a classical example of contact of two bodies, the proposed approach is applied for verification of the obtained results.
Compliant Contact Force Model, Multibody Systems, Stiffness and Damping Coefficients, Neural Network
To cite this article
Identification of Compliant Contact Force Parameters in Multibody Systems Based on the Neural Network Approach Related to Municipal Property Damages, American Journal of Neural Networks and Applications.
Vol. 3, No. 5,
2017, pp. 49-55.
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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