The modelling of the dynamic behavior of hysteretic materials and devices must take into account magnetodynamic effects. In the present paper these tasks are simultaneously modelled by means of an ad-hoc Neural System (NS) based on an array of 3-input 1-output Feed Forward NNs. Each NN is aimed to a particular typology of the excitation field (prediction of flux density from a known waveform of the magnetic field strength or vice-versa) and manages just a fixed portion of the dynamic hysteresis loop. The whole hysteretic curve is simulated by linking the evaluations made by different NNs of the NS. The NS is able to perform the simulation of any kind of dynamic loop (saturated and non-saturated, symmetric or asymmetric) generated by any assigned arbitrarily distorted excitations into a fixed range of frequencies. Numerical validations are presented both on a "virtual magnetic device" and on a non-oriented Fe-(3 wt%) Si laminations (thickness 0.35 mm)

Dynamic hysteresis modelling of magnetic materials by using a neural network approach

LAUDANI, ANTONINO;
2015-01-01

Abstract

The modelling of the dynamic behavior of hysteretic materials and devices must take into account magnetodynamic effects. In the present paper these tasks are simultaneously modelled by means of an ad-hoc Neural System (NS) based on an array of 3-input 1-output Feed Forward NNs. Each NN is aimed to a particular typology of the excitation field (prediction of flux density from a known waveform of the magnetic field strength or vice-versa) and manages just a fixed portion of the dynamic hysteresis loop. The whole hysteretic curve is simulated by linking the evaluations made by different NNs of the NS. The NS is able to perform the simulation of any kind of dynamic loop (saturated and non-saturated, symmetric or asymmetric) generated by any assigned arbitrarily distorted excitations into a fixed range of frequencies. Numerical validations are presented both on a "virtual magnetic device" and on a non-oriented Fe-(3 wt%) Si laminations (thickness 0.35 mm)
2015
978-8-8872-3724-5
Magnetic Hysteresis
Magnetic losses
Magnetodynamic
Neural Networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/575388
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