Modeling the sensitivity of GMI samples by neural networks

Over the past few years, several studies have been developed in order to quantitatively model the GMI effect (Giant Magnetoimpedance). However, these models adopt simplifications that significantly affect its theoretical- experimental performance and its generalization capability, and models that incorporate parameters that generate asymmetry - AGMI (asymmetric GMI) - such as the DC level of the excitation current of the GMI samples are still rare. This work aims to develop a new model, sufficiently general, which also incorporates the asymmetry induced by the DC level of the excitation current, capable of guiding the experimental procedures of characterization of the GMI samples. Thus, this paper proposes, presents and discusses the use of a computational model based on feedforward Multilayer Perceptron Neural Networks to model the impedance magnitude sensitivity and impedance phase sensitivity, of the GMI effect, as functions of the magnetic field, for Co 70Fe 5Si 15B 10 ferromagnetic amorphous alloys. The proposed model allows obtaining these sensitivities based on some of the main parameters that affect it: length of the samples, DC level and frequency of the excitation current and the external magnetic field.