|
Descripción
|
|
|---|---|
| This work presents the analysis of the factors which influence the precision of a glucose predictor based on neural networks (NNM). The NNM was individually trained with continuous glucose data from nine patients. The precision was calculated as the root mean square error (RMSE) between the original and the predicted profiles (Pérez-Gandía, 2008). This work analyzes the factors that influence the precision variability and the optimum training-set size to improve the model performance. | |
|
Internacional
|
No |
|
Nombre congreso
|
XXVII Congreso Anual de la Sociedad Española de Ingeniería Biomédica |
|
Tipo de participación
|
960 |
|
Lugar del congreso
|
CÁDIZ |
|
Revisores
|
Si |
|
ISBN o ISSN
|
978-84-608-0990-6 |
|
DOI
|
|
|
Fecha inicio congreso
|
18/11/2009 |
|
Fecha fin congreso
|
20/11/2009 |
|
Desde la página
|
413 |
|
Hasta la página
|
416 |
|
Título de las actas
|
LIBRO DE ACTAS |