1.
Sensors (Basel)
; 19(6)2019 Mar 17.
Artigo
em Inglês
| MEDLINE
| ID: mdl-30884877
RESUMO
In this paper, a deep neural network based model for a set of small-scale magnetorheological dampers (MRD) is developed where relevant parameters that have a physical meaning are inputs to the model. An experimental platform and a 3D-printing rapid prototyping facility provided a set of different conditions including MRD filled with two different MR fluids, which were used to train a Deep Neural Network (DNN), which is the core of the proposed model. Testing results indicate the model could forecast the hysteretic response of magnetorheological dampers for different load conditions and various physical configurations.
2.
Comput Intell Neurosci
; 2016: 3263612, 2016.
Artigo
em Inglês
| MEDLINE
| ID: mdl-27956895