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.
|Titolo:||3D ELF magnetic field strength modeling through fully connected cascade networks|
|Data di pubblicazione:||2016|
|Citazione:||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..|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|