Title :
Solution of non-linear forward problems in electrical capacitance tomography using neural networks
Author :
Marashdeh, Q. ; Warsito, W. ; Fan, L.S. ; Teixeira, F.L.
Author_Institution :
Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA
Abstract :
A feedforward neural network (NN) is used to solve non-linear forward problems in electrical capacitance tomography (ECT). Data front ECT measurements is used to train and test an NN solution. The NN approach of solving forward problems is based on training a network with pre-known capacitance data corresponding to different permittivity distributions. The network is then used to predict forward problem solutions for various permittivity distributions. The output is also compared to results from linearization methods through iterative image reconstruction.
Keywords :
capacitance; feedforward neural nets; image reconstruction; imaging; inverse problems; iterative methods; learning (artificial intelligence); linearisation techniques; permittivity; permittivity measurement; tomography; electrical capacitance tomography; feedforward neural network; inverse problems; iterative image reconstruction; linearization methods; nonlinear forward problems; permittivity distributions; real time imaging; training; Capacitance measurement; Capacitive sensors; Electrical capacitance tomography; Feedforward neural networks; Intelligent networks; Inverse problems; Iterative algorithms; Neural networks; Permittivity; Pixel;
Conference_Titel :
Antennas and Propagation Society International Symposium, 2005 IEEE
Print_ISBN :
0-7803-8883-6
DOI :
10.1109/APS.2005.1551276
