The Optimization of Inverse problems for Two-Dimensional Conductors.

• Main Authors: Wei Chien, 錢威
• Other Authors: Chien-Ching Chiu
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/74844233305494366084

博士 === 淡江大學 === 電機工程學系博士班 === 94 === The thesis presents three related aspects of computational approach to the imaging of a conducting cylinder. In the first one, an imperfect conducting cylinder of unknown shape and variable conductivity is considered. Two different cases of inverse problem in free space and half space have done respectively. In the second one, cubic-spline method and trigonometric series for shape description are used and compared in several different situations (half space, partially immersed, slab medium, and periodic conductor in free space). In the third one, the inverse scattering problem is addressed to discuss the CPU time for reconstructing a perfectly conducting cylinder for two different cases (half space and slab medium). It is solved by the improved Steady State Genetic Algorithm (SSGA) and Simple Genetic Algorithm (SGA) and the consuming time in finding out the global extreme solution of the objective function is compared. Based on the boundary conditions and the measured scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an III optimization problem. In the first one, the genetic algorithm is employed to find out the global extreme solution of the objective function. Numerical results demonstrate that, even when the initial guess is far away from the exact one, a good reconstruction has been obtained. In the second one, the shape of the scatterer described by using cubic-spline method can be reconstructed. In such case, Fourier series expansion will fail. Numerical results show that the shape description by using cubic-spline method is much better than that Fourier series. In the third one, it is found that the searching ability of SSGA is much powerful than that of the SGA. The consuming time for converging to a global extreme solution by using SSGA is much less than that SGA. Numerical results show that the image reconstruction problem by using SSGA is much better than by SGA in time consuming.