Prediction of dynamic hysteresis loops of nano-crystalline cores

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Date

2009-03

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Pergamon-Elsevier Science

Abstract

Dynamic hysteresis loops of a range of nano-crystalline cores have been obtained over a wide frequency range (1-50 kHz). A dynamic hysteresis model front measurements using an artificial neural network trained by the delta-bar-delta learning algorithm has been developed. The input parameters include the geometrical dimensions of cores, peak magnetic induction and magnetizing frequency. The results show the neural network model has an acceptable estimation capability for dynamic hysteresis loops of toroidal nano-crystalline cores.

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Keywords

Dynamic hysteresis modelling, Nano-crystalline cores, Neural network, Toroidal cores, Computer science, Engineering, Operations research & management science, Crystalline materials, Hysteresis loops, Magnetic levitation vehicles, Magnetic materials, Neural networks, Delta-bar-delta, Dynamic hysteresis loops, Dynamic hysteresis modeling, Dynamic hysteresis modelling, Geometrical dimensions, Nanocrystalline cores, Neural network model, Wide frequency range, Hysteresis

Citation

Hacıismailoğlu, M. C. vd. (2009). "Prediction of dynamic hysteresis loops of nano-crystalline cores". Expert Systems with Applications, 36(2), Part 1, 2225-2227.