TY - GEN
T1 - Continuous neural networks and finite element application for the tissue deformation reconstruction dynamic
AU - Fuentes, Rita Q.
AU - Poznyak, Alexander
AU - Figueroa, Ivan
AU - Garcia, Alejandro
AU - Chairez, Isaac
PY - 2012
Y1 - 2012
N2 - This paper presents the nonparametric modeling based in differential neural networks (DNN) of soft tissue deformation dynamic under a single external pressure force. The construction of the DNN-adaptive model is based on the finite element method (FEM), the proposal is to make that every element be approximate by a DNN. The DNN input is taken by the nodes information collected from real experimental data captured from a variable-velocity electro-mechanical platform applying a single-point force to a tissue sample, in this way, an assembled DNN is used to join the element DNNs to obtain the complete system modeling. To verify the qualitative behavior of the suggested methodology, here the estimated trajectories are compared with the Motion Capture spatial position vector of the surface of the sample tissue. The adaptive laws for weights ensure the closeness of DNN trajectories to the tissue dynamics.
AB - This paper presents the nonparametric modeling based in differential neural networks (DNN) of soft tissue deformation dynamic under a single external pressure force. The construction of the DNN-adaptive model is based on the finite element method (FEM), the proposal is to make that every element be approximate by a DNN. The DNN input is taken by the nodes information collected from real experimental data captured from a variable-velocity electro-mechanical platform applying a single-point force to a tissue sample, in this way, an assembled DNN is used to join the element DNNs to obtain the complete system modeling. To verify the qualitative behavior of the suggested methodology, here the estimated trajectories are compared with the Motion Capture spatial position vector of the surface of the sample tissue. The adaptive laws for weights ensure the closeness of DNN trajectories to the tissue dynamics.
KW - Differential Neural Networks
KW - Finite Element Method
KW - partial differential equations
KW - tissue deformation
UR - http://www.scopus.com/inward/record.url?scp=84880759566&partnerID=8YFLogxK
U2 - 10.1109/Andescon.2012.44
DO - 10.1109/Andescon.2012.44
M3 - Contribución a la conferencia
AN - SCOPUS:84880759566
SN - 9780769548821
T3 - Proceedings of the 6th Andean Region International Conference, Andescon 2012
SP - 157
EP - 160
BT - Proceedings of the 6th Andean Region International Conference, Andescon 2012
T2 - 6th Andean Region International Conference, Andescon 2012
Y2 - 7 November 2012 through 9 November 2012
ER -