Abstract
This paper deals with a prediction of a giant magneto-impedance (GMI) effect on amorphous micro-wires using an artificial neural network (ANN). The prediction model has three hidden layers with fifteen neurons and full connectivity between them. The ANN model is used to predict the GMI effect for Co70.3Fe3.7B10Si13Cr3 glass-coated micro-wire. The results show that the ANN model has a 98.99% correlation with experimental data.
Highlights
The origin of the giant magneto-impedance (GMI) effect is attributed to a combination of the skin effect and magnetic domain behavior in soft ferromagnetic material
As shown in Figure, the values obtained through the training of the artificial neural network (ANN) model are very close to experimental results, indicating a strong correlation between the input and output parameters of the ANN model
The proposed model developed from experimental data obtained previously can be used to predict more the GMI curves for Co
Summary
The origin of the GMI effect is attributed to a combination of the skin effect and magnetic domain behavior in soft ferromagnetic material. The GMI ratios have been calculated as. Are the GMI ratio, magneto impedance at magnetic field H. And magneto impedance at maximum magnetic field, respectively. The discovery of the giant magneto-impedance (GMI) effect in soft ferromagnetic amorphous micro-wires makes them very attractive candidate material for making high-performance magnetic sensors [ – ]. The use of such micro-wires could have implications for the generation of electronic devices, which will involve increasingly smaller components [ – ]
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