By Topic

Automated two-dimensional field computation in nonlinear magnetic media using Hopfield neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Adly, A.A. ; Fac. of Eng., Cairo Univ., Giza, Egypt ; Abd-El-Hafiz, S.K.

It is well known that the computation of magnetic fields in nonlinear magnetic media may be carried out using different approaches. In the case of problems involving complex geometries and/or magnetic media, numerical techniques become especially more appealing. In this paper, we present an automated integral equation approach using which two-dimensional field computations may be carried out in nonlinear magnetic media. This approach is constructed in terms of a continuous Hopfield neural network (HNN) whose neuron activation functions are set to mimic the vectorial magnetic properties of the media. Using well-established HNN energy minimization algorithms, an automated solution of the problem is then obtained. The approach has been implemented and resulted in good agreement with finite-element (FE) computations. Details of the approach, computations, and FE results are given in this paper.

Published in:

Magnetics, IEEE Transactions on  (Volume:38 ,  Issue: 5 )