Publication: Dynamic hysteresis modelling for nano-crystalline cores
Date
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Küçük, İlker Semih
Hacıismailoğlu, Muhammed Cüneyt
Derebaşı, Naim
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Pergamon-Elsevier Science
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Abstract
This paper presents all artificial neural network approach based oil dynamic Preisach model to compute hysteresis loops of nano-crystalline cores. The network has been trained by a Levenberg-Marquardt learning algorithm. The model is fast and does not require tremendous computational efforts. The results obtained by using the proposed model are in good agreement with experimental results.
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Keywords
Dynamic hysteresis model, Nano-crystal, Neural network, Neural-network, Genetic algorithm, Toroidal cores, Power losses, Prediction, Computer science, Engineering, Operations research & management science, Crystalline materials, Hysteresis loops, Neural networks, Artificial neural network approach, Computational effort, Dynamic hysteresis modeling, Dynamic hysteresis modelling, Levenberg-Marquardt learning algorithms, Nanocrystalline cores, ON dynamics, Hysteresis
Citation
Küçük, İ. S. vd. (2009). "Dynamic hysteresis modelling for nano-crystalline cores". Expert Systems with Applications, 36(2), 3188-3190.