Elektrik Empedans Tomografide Eliptik Yapılardaki İletkenlik Dağılımları Geriçatım Probleminin Yapay Sinir Ağları ile İncelenmesi ; Analysıs Of Conductıvıty Dıstrıbutıon Reconstructıon Problem Of Ellıptıc Models Wıth Artıfıcıal Neural Networks At Electrıcal Impedance Tomography

Electric Impedance Tomography is the method that shows the conductivity distribution in any geometry. In this study, the image of the conductivity distribution in circle and ellipse geometries is tried to be obtained. Forward problem and inverse problem solutions of EIT are discussed. The forward problem of EIT is linear and stable. The Finite Element Method is used for solving forward problem of circle and ellipse geometries. The analytical solution and FEM results of geometries were compared and it was shown that FEM can be applied in different geometries. The inverse problem of EIT is a nonlinear and ill-posed problem where the accuracy of the solution is discussed. Due to this structure, the problem has been evaluated and solved in both linear and nonlinear manner. The inverse problem solution is also known as the image reconstruction. Optimization techniques in general and artificial neural network models can be used for such inverse problem solving. Specifically, for circle and ellipse geometries, their conductivity values were studied by using virtual voltage values obtained through specially designed electrode patterns which are supposed to be placed around the geometry. Using FEM used for the forward solution, a database can be generated with inhomogeneous distributions specified in different locations and conductivities. Artificial neural network has been trained by using tension values obtained from the surface and distributions under different conductivity scenarios. Training input for those cases are measured voltage values observed from the boundary. At the end of the learning stage, the trained neural network was tested and error values were tabulated. Within the framework of artificial neural network structures, mainly Radial Based Neural Networks and Multilayer Perceptron were selected and compared with the patterns obtained by selected optimization methods. In order to provide comparative results, neural network structures were trained by using generated 69 different patterns to get ...