Neuro-genetic system for optimization of GMI samples sensitivity

Neural Networks : the Official Journal of the International Neural Network Society
A C O Pitta BotelhoE Costa Silva

Abstract

Magnetic sensors are largely used in several engineering areas. Among them, magnetic sensors based on the Giant Magnetoimpedance (GMI) effect are a new family of magnetic sensing devices that have a huge potential for applications involving measurements of ultra-weak magnetic fields. The sensitivity of magnetometers is directly associated with the sensitivity of their sensing elements. The GMI effect is characterized by a large variation of the impedance (magnitude and phase) of a ferromagnetic sample, when subjected to a magnetic field. Recent studies have shown that phase-based GMI magnetometers have the potential to increase the sensitivity by about 100 times. The sensitivity of GMI samples depends on several parameters, such as sample length, external magnetic field, DC level and frequency of the excitation current. However, this dependency is yet to be sufficiently well-modeled in quantitative terms. So, the search for the set of parameters that optimizes the samples sensitivity is usually empirical and very time consuming. This paper deals with this problem by proposing a new neuro-genetic system aimed at maximizing the impedance phase sensitivity of GMI samples. A Multi-Layer Perceptron (MLP) Neural Network is used to mo...Continue Reading

References

Jun 1, 1993·Physical Review. B, Condensed Matter·K Mandal, S K Ghatak
Feb 7, 2008·IEEE Transactions on Neural Networks·J Nie, S Haykin
Sep 1, 2014·The Review of Scientific Instruments·E Costa SilvaE Costa Monteiro

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