Abstract

Abstract-In this paper, a technique is introduced by which to extend the applicability of the existing analytic solutions of electromagnetic field problems to cases where random-noisy-sampled data (such as measurement outputs) are available, rather than analytic input functions. We address those problems for which a theoretical solution exists in the form of a superposi-tion of some bash functions. The algorithm introduced employs this same set of basis functions, and finds the expansion coefficients by the use of an iterative error-minimization tech-nique, which resembles those found in the process of training of artificial neural networks. In cases where the expansion func-tions are orthonormal, guaranteed fast convergence is proved. As well, we show how neuro-computation techniques can be employed to circumvent the effects of various types of measure-ment errors and noise. Satisfactory performance of the algo-rithm is shown for a test problem driven by random inputs cor-rupted with various levels of Gaussian noise.