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.

3D ELF magnetic field strength modeling through fully connected cascade networks

Coco S;
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.
978-8-8872-3730-6
Magnetic Fields; Modeling; Neural Networks; Fully Connected Cascade
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/72463
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