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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Volume were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2003, 8(2), 217-223; doi:10.3390/mca8020217

Prediction of Magnetic Properties of Strip Wound Toroidal Cores up to 2 Khz Using Artificial Neural Network

Uludag University, Arts and Sciences Faculty, Physics Department, 16059 Gorukie Bursa, Turkey
Authors to whom correspondence should be addressed.
Published: 1 August 2003
Download PDF [780 KB, uploaded 31 March 2016]


Although magnetic wound cores have simple geometries, their magnetic properties vary in a complex manner depending on core geometry and dimensions etc. These parameters have a strong influence on magnetic performance of wound toroidal cores made from electrical steels or similar strip products. Through theoretical evaluation and experimental measurements carried out over a few years, magnetic performance of a range of strip wound cores have been quantified at low and high frequency. Using this information a neural network model has been developed for prediction core magnetic field strength, power loss and permeability a wide range of flux density and frequency. Input parameters include variables such as core geometry; dimensions strip width and thickness, induction frequency and flux density. The developed network provides flexibility in the choice of training parameters. transfer functions and training algorithm thereby enhancing accuracy.
Keywords: Magnetic properties; toroidal cores; neural network; electrical steels Magnetic properties; toroidal cores; neural network; electrical steels
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Derebasi, N.; Kucuk, I. Prediction of Magnetic Properties of Strip Wound Toroidal Cores up to 2 Khz Using Artificial Neural Network. Math. Comput. Appl. 2003, 8, 217-223.

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