Computational modeling of GMI effect in Co-based amorphous ribbons

Asli Ayten Kaya

doi:10.1186/1029-242x-2013-293

Abstract

This paper presents a prediction of a giant magneto-impedance (GMI) effect on Co-based amorphous ribbons using an artificial neural network (ANN) approach based on a self-organizing feature map (SOFM). The input parameters included the compositions of Fe and Co, ribbon width and magnetizing frequency. The output parameter was the GMI effect. The results show that the proposed model can be used for estimation of the GMI effect in the amorphous ribbons.

Highlights

When a soft ferromagnetic conductor is subjected to an alternating current, a large change in the complex impedance of the conductor can be achieved upon applying a magnetic field
The giant magneto-impedance (GMI) effects are modeled using self-organizing feature map (SOFM) and previous experimental data [, ] of amorphous ribbons made from Co Fe Si B and Co
Si B ) using artificial neural networks. To achieve this goal, magnetizing field (H), ribbon width (l), magnetizing frequency (f ), concentration of Co (Co%) and concentration of Fe (Fe%) were used as the input of networks, and GMI% data points were used as the output of these networks

Summary

Introduction

When a soft ferromagnetic conductor is subjected to an alternating current, a large change in the complex impedance of the conductor can be achieved upon applying a magnetic field. The GMI effects are modeled using self-organizing feature map (SOFM) and previous experimental data [ , ] of amorphous ribbons made from Co Fe Si B and Co . SOFM is a special neural network that accepts N -dimensional input vectors and maps them to the Kohonen layer, in which neurons are organized in an L-dimensional lattice (grid) representing the feature space.

Results

Conclusion