Next Article in Journal
Determination of Hardness of Pre-Aged AA 6063 Aluminum Alloy by Means of Artificial Neural Networks Method
Previous Article in Journal
Differential Transform Technique for Solving Fifth-Order Boundary Value Problems
Article Menu

Article Versions

Export Article

Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with the previous journal publisher.
Open AccessArticle
Math. Comput. Appl. 2003, 8(2), 201-208; https://doi.org/10.3390/mca8020201

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

Uludag University, Arts and Sciences Faculty, Physics Department, 16059 Gorukle-Bursa, Turkey
*
Authors to whom correspondence should be addressed.
Published: 1 August 2003
Download PDF [651 KB, uploaded 31 March 2016]

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
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Math. Comput. Appl. EISSN 2297-8747 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top