Continuous neural networks and finite element application for the tissue deformation reconstruction dynamic

Rita Q. Fuentes, Alexander Poznyak, Ivan Figueroa, Alejandro Garcia, Isaac Chairez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 6th Andean Region International Conference, Andescon 2012
Páginas157-160
Número de páginas4
DOI
EstadoPublicada - 2012
Evento6th Andean Region International Conference, Andescon 2012 - Cuenca, Ecuador
Duración: 7 nov. 20129 nov. 2012

Serie de la publicación

NombreProceedings of the 6th Andean Region International Conference, Andescon 2012

Conferencia

Conferencia6th Andean Region International Conference, Andescon 2012
País/TerritorioEcuador
CiudadCuenca
Período7/11/129/11/12

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