the present work documents the research towards the development of an efficient, fast and reliable black-box model to assess the magnetic field at extremely low frequency in a given volume. The approach is based on the implementation of an array of neural networks (aggregated/bootstrapped) trained on suitably conditioned experimental measurements. To enhance the computational performance of the method, a newly developed architecture was used for the neural networks: the fully connected cascade. To validate the approach, the same implementation using classic Feed Forward networks is compared in terms of both computational costs and precision.
Coco, S., Laudani, A., Lozito, G.M., RIGANTI FULGINEI, F., Salvini, A. (2016). 3D ELF magnetic field strength modeling through fully connected cascade networks. In AEIT 2016 - International Annual Conference: Sustainable Development in the Mediterranean Area, Energy and ICT Networks of the Future (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.23919/AEIT.2016.7892807].
3D ELF magnetic field strength modeling through fully connected cascade networks
COCO, SALVATORE;LAUDANI, ANTONINO;LOZITO, GABRIELE MARIA;RIGANTI FULGINEI, Francesco;SALVINI, Alessandro
2016-01-01
Abstract
the present work documents the research towards the development of an efficient, fast and reliable black-box model to assess the magnetic field at extremely low frequency in a given volume. The approach is based on the implementation of an array of neural networks (aggregated/bootstrapped) trained on suitably conditioned experimental measurements. To enhance the computational performance of the method, a newly developed architecture was used for the neural networks: the fully connected cascade. To validate the approach, the same implementation using classic Feed Forward networks is compared in terms of both computational costs and precision.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
