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Article

Neural Network Using Genetic Algorithm for Magnetic Performance Prediction of Toroidal Wound Cores at 50 Hz

by
Ilker Kucuk
* and
Naim Derebasi
*
Uludag University, Arts and Sciences Faculty, Physics Department, 16059 Gorukle-Bursa, Turkey
*
Authors to whom correspondence should be addressed.
Math. Comput. Appl. 2003, 8(2), 201-208; https://doi.org/10.3390/mca8020201
Published: 1 August 2003
Versions Notes

Abstract

Geometrical and building parameters have a strong influence on magnetic performance of toroidal wound cores made from grain oriented 3% SiFe electrical steel. From a sample of 40 cores with dimensions ranging from 35 to 160 mm outer diameter, 25 to 100 mm inner diameter and JO to 70 mm strip width and a flux density range of 0.1 to 1.7 T have been obtained and used as training data to a generalised feedforward neural network.
Keywords: Artificial neural network; Genetic algorithm; toroidal wound core; magnetic performance Artificial neural network; Genetic algorithm; toroidal wound core; magnetic performance

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MDPI and ACS Style

Kucuk, I.; Derebasi, N. Neural Network Using Genetic Algorithm for Magnetic Performance Prediction of Toroidal Wound Cores at 50 Hz. Math. Comput. Appl. 2003, 8, 201-208. https://doi.org/10.3390/mca8020201

AMA Style

Kucuk I, Derebasi N. Neural Network Using Genetic Algorithm for Magnetic Performance Prediction of Toroidal Wound Cores at 50 Hz. Mathematical and Computational Applications. 2003; 8(2):201-208. https://doi.org/10.3390/mca8020201

Chicago/Turabian Style

Kucuk, Ilker, and Naim Derebasi. 2003. "Neural Network Using Genetic Algorithm for Magnetic Performance Prediction of Toroidal Wound Cores at 50 Hz" Mathematical and Computational Applications 8, no. 2: 201-208. https://doi.org/10.3390/mca8020201

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