Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008)

A New Image Reconstruction Algorithm for Electrical Capacitance Tomography

Authors
Yan Li1, Peiquan Bao, Peng Cui, Li Feng, Liyong Zhang
1Harbin University of Science and Technology
Corresponding Author
Yan Li
Available Online December 2008.
DOI
https://doi.org/10.2991/jcis.2008.114How to use a DOI?
Keywords
Electrical capacitance tomography; RBF; Adaptive genetic algorithm; Tikhonov regularization; Image reconstruction
Abstract

In view of low precision of the reconstructed image of Electrical Capacitance Tomography (ECT) at present, a new image reconstruction method based on RBF neural network for Electrical Capacitance Tomography is proposed. Adaptive genetic algorithm is used to optimize the centers and widths of hidden layer of RBF network and Tikhonov regularization method is used to train the weights of RBF network. The simulation results for 12-electrode electrical capacitance tomography system illustrate that this method can improve the quality of reconstruction image obviously, testify the effectiveness of the proposed method.

Copyright
© 2008, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008)
Series
Advances in Intelligent Systems Research
Publication Date
December 2008
ISBN
978-90-78677-18-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2008.114How to use a DOI?
Copyright
© 2008, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yan Li
AU  - Peiquan Bao
AU  - Peng Cui
AU  - Li Feng
AU  - Liyong Zhang
PY  - 2008/12
DA  - 2008/12
TI  - A New Image Reconstruction Algorithm for Electrical Capacitance Tomography
BT  - Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008)
PB  - Atlantis Press
SP  - 678
EP  - 683
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.114
DO  - https://doi.org/10.2991/jcis.2008.114
ID  - Li2008/12
ER  -