Yayın: Dynamic hysteresis modelling for nano-crystalline cores
Tarih
Kurum Yazarları
Küçük, İlker Semih
Hacıismailoğlu, Muhammed Cüneyt
Derebaşı, Naim
Yazarlar
Danışman
Dil
Türü
Yayıncı:
Pergamon-Elsevier Science
Dergi Başlığı
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Cilt Başlığı
Özet
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.
Açıklama
Kaynak:
Anahtar Kelimeler:
Konusu
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
Alıntı
Küçük, İ. S. vd. (2009). "Dynamic hysteresis modelling for nano-crystalline cores". Expert Systems with Applications, 36(2), 3188-3190.