A neural network-based tool for magnetic performance prediction of toroidal cores

Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/25292

Title:	A neural network-based tool for magnetic performance prediction of toroidal cores
Authors:	Miti, G.K. Moses, Anthony John Fox, David Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü. Derebaşı, Naim 11540936300
Keywords:	Materials science Physics Artificial intelligence Magnetic losses Neural networks Soft magnetic materials Strip-wound cores Magnetic leakage Magnetic permeability Toroidal cores Magnetic cores
Issue Date:	Jan-2003
Publisher:	Elsevier
Citation:	Miti, G. K. vd. (2003). “A neural network-based tool for magnetic performance prediction of toroidal cores”. Journal of Magnetism and Magnetic Materials, 254(Special Issue), 262-264.
Abstract:	Geometrical and building parameters have a strong influence on magnetic performance of wound toroidal cores made from electrical steel or similar strip products. This paper presents a neural network-based approach to predict losses and permeability in such cores of varying geometries over an induction range of 0.2-1.8T (50Hz). The approach is shown to be successful.
Description:	Bu çalışma, 05-07 Eylül 2001 tarihleri arasında Bilbao[İspanya]’da düzenlenen 15. International Symposium on Soft Magnetic Materials’da bildiri olarak sunulmuştur.
URI:	https://doi.org/10.1016/S0304-8853(02)00788-6 http://hdl.handle.net/11452/25292
ISSN:	0304-8853
Appears in Collections:	Scopus Web of Science

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