By Topic

Two ANN reconstruction methods for electrical impedance tomography

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

3 Author(s)

Two Artificial Neural Network (ANN) reconstruction methods for Electrical Impedance Tomography (EIT) have been presented in this paper. The problem under study concerns the reconstruction of the conductivity distribution inside the investigated area, using the information collected from the boundary. The first approach consists in ANN learning using electrical potential vectors, which were obtained from numerical solution of the forward problems. The second method using a standard feed-forward multilayered neural networks, applies the circuit representation for the finite clement discretization. Using the quadrilateral finite element, the neural network structure for EIT problem has been proposed. The advantages and disadvantages both methods with respect to classical approach are discussed in detail

Published in:

Magnetics, IEEE Transactions on  (Volume:34 ,  Issue: 5 )