Yayın: Multilayered perceptron neural networks to compute energy losses in magnetic cores
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Küçük, İlker
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Elsevier
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Özet
This paper presents a new approach based on multilayered perceptrons (MLPs) to compute the specific energy losses of toroidal wound cores built from 3% SiFe 0.27 mm thick M4, 0.1 and 0.08 mm thin gauge electrical steel strips. The MLP has been trained by a back-propagation and extended delta-bar-delta learning algorithm. The results obtained by using the MLP model were compared with a commonly used conventional method. The comparison has shown that the proposed model improved loss estimation with respect to the conventional method.
Açıklama
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Konusu
Materials science, Physics, Toroidal wound cores, Neural network, Energy losses, Mathematical models, Magnetic properties, Magnetic materials, Learning algorithms, Energy dissipation, Backpropagation, Toroidal wounds, Multilayered perceptrons (MLP), Delta-bar-delta learnings, Multilayer neural networks, Toroidal cores
Alıntı
Küçük, İ. (2006). ''Multilayered perceptron neural networks to compute energy losses in magnetic cores''. Journal of Magnetism and Magnetic Materials, 307(1), 53-61.