1. Açık Erişim@BUU
  2. İndeksli Yayınlar
  3. Web of Science
Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/25185
Title: Dynamic hysteresis modelling for nano-crystalline cores
Authors: Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü.
0000-0002-0781-3376
0000-0003-2546-0022
Küçük, İlker Semih
Hacıismailoğlu, Muhammed Cüneyt
Derebaşı, Naim
K-7950-2012
AAI-2254-2021
6602910810
8975743500
11540936300
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
Issue Date: Mar-2009
Publisher: Pergamon-Elsevier Science
Citation: Küçük, İ. S. vd. (2009). "Dynamic hysteresis modelling for nano-crystalline cores". Expert Systems with Applications, 36(2), 3188-3190.
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.
URI: https://doi.org/10.1016/j.eswa.2008.01.084
https://www.sciencedirect.com/science/article/pii/S0957417408000997
http://hdl.handle.net/11452/25185
ISSN: 0957-4174
Appears in Collections:Scopus
Web of Science

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