By Topic

Direct solution method for finite element analysis using Hopfield neural network

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
$33 $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

4 Author(s)
H. Yamashita ; Fac. of Eng., Hiroshima Univ., Japan ; N. Kowata ; V. Cingoski ; K. Kaneda

One property of the Hopfield neural network is the monotonous minimization of energy as time proceeds. In this paper, this property is applied to minimize the energy functional obtained by ordinary finite element analysis. The mathematical representation and correlation between finite element and neural network calculus are presented. The selection of the sigmoid function and its influence on the iteration process is discussed. The obtained results using the proposed method show excellent agreement with theoretical solutions

Published in:

IEEE Transactions on Magnetics  (Volume:31 ,  Issue: 3 )