DocumentCode
2342498
Title
RBF neural network image reconstruction for electrical impedance tomography
Author
Wang, Chao ; Lang, Jian ; Wang, Hua-Xiang
Author_Institution
Sch. of Electr. Eng. & Autom., Tianjin Univ., China
Volume
4
fYear
2004
fDate
26-29 Aug. 2004
Firstpage
2549
Abstract
Reconstruction of images in electrical impedance tomography requires the solution of a nonlinear inverse problem. This work presents a RBF neural network image reconstruction method trained by the genetic algorithm. The genetic algorithm is used to search for the optimum values of the following three parameters in the RBF network: centers, variances and connection weights, which are encoded as real number. Experimental results illustrate that this method can markedly improve image quality.
Keywords
electric impedance measurement; genetic algorithms; image reconstruction; neural nets; radial basis function networks; tomography; RBF neural network; electrical impedance tomography; genetic algorithm; image reconstruction; nonlinear inverse problem; Conductivity measurement; Electrodes; Genetic algorithms; Image reconstruction; Impedance; Inverse problems; Neural networks; Radial basis function networks; Tomography; Voltage;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1382233
Filename
1382233
Link To Document