TY - GEN
T1 - The implementation of FEM and RBF neural network in EIT
AU - Wang, Peng
AU - Li, Hong Li
AU - Xie, Li Li
AU - Sun, Yi Cai
PY - 2009
Y1 - 2009
N2 - With the rapid development of electronic technology, semiconductor section resistivity measurement is receiving increasing attention. This paper applies electrical impedance tomography (EIT) technology to semiconductor resistivity measurements. FEM is applied to solve the EIT forward problem. Mathematical description of partial differential equation, equivalent variation differential problem, element characteristic matrix and the assembly rule of general matrix are given for calculation. To solve the EIT inverse problem, a new method of Image reconstruction algorithm based on RBF neural network is proposed. This method can well adapt to non-linear and ill-posed characteristics of EIT. The simulation experiment results indicate that the RBF algorithm can improve the reconstruction image's quality and the accuracy obviously.
AB - With the rapid development of electronic technology, semiconductor section resistivity measurement is receiving increasing attention. This paper applies electrical impedance tomography (EIT) technology to semiconductor resistivity measurements. FEM is applied to solve the EIT forward problem. Mathematical description of partial differential equation, equivalent variation differential problem, element characteristic matrix and the assembly rule of general matrix are given for calculation. To solve the EIT inverse problem, a new method of Image reconstruction algorithm based on RBF neural network is proposed. This method can well adapt to non-linear and ill-posed characteristics of EIT. The simulation experiment results indicate that the RBF algorithm can improve the reconstruction image's quality and the accuracy obviously.
KW - Electrical impedance tomography
KW - Finite element method
KW - RBF neural network
KW - Semiconductor section resistivity
UR - https://www.scopus.com/pages/publications/77949495740
U2 - 10.1109/ICINIS.2009.26
DO - 10.1109/ICINIS.2009.26
M3 - Conference contribution
AN - SCOPUS:77949495740
SN - 9780769538525
T3 - ICINIS 2009 - Proceedings of the 2nd International Conference on Intelligent Networks and Intelligent Systems
SP - 66
EP - 69
BT - ICINIS 2009 - Proceedings of the 2nd International Conference on Intelligent Networks and Intelligent Systems
T2 - 2nd International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2009
Y2 - 1 November 2009 through 3 November 2009
ER -