Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
