Application of evolutionary algorithms and neural networks to electromagnetic inverse problems

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.Includes bibliographical references (leaves 72-74).Issued also on microfiche from Lange Micrographics.This research investigates the imaging of buried two-dimensional objects (conducting cylinders and air tunnels) of various shapes, by processing the scattered electromagnetic field under Transverse Magnetic (TM) and Transverse Electric (TE) illumination. A technique is developed for the novel application of the Differential Evolution (DE) algorithm to electromagnetic imaging of buried objects. A hybrid of the DE and Powell method is also developed to further accelerate the DE's performance. Both plane wave and line source excitations are employed for a circular and cross-borehole configuration of receivers. The effect of noise and the simultaneous recovery of shape and location of the objects are also investigated. Simulation results are presented which show that this technique is efficient and robust compared to state-of-the-art methods. A significant achievement in the area of real time inversion is made possible by training a neural network for recovery of shape and location. Test results presented indicate high reliability of the network