Neuro-computation techniques in sampled-data electromagnetic field problems

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 superposition of some basis functions. The algorithm introduced employs this same set of basis functions, and finds the expansion coefficients by the use of an iterative error-minimization technique, which resembles those found in the process of training of artificial neural networks. In cases where the expansion functions 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 measurement errors and noise. Satisfactory performance of the algorithm is shown for a test problem driven by random inputs corrupted with various levels of Gaussian noise