Applying Neuro-Fuzzy Soft Computing Techniques to the Circular Loop Antenna Radiation Problem

Abstract

Analytical methods used to solve the circular loop antenna radiation problem are effective and accurate, but also time-consuming, due to the complex mathematical background. However, soft computing techniques do not require complex mathematical procedures and are more straightforward and fast. In order to solve the circular loop antenna radiation problem, we examine two methods based on artificial intelligence and fuzzy logic. Different neural network learning algorithms are examined, and the fuzzy inference system parameters are identified. Extensive numerical tests show that the predicted values are consistent with those calculated from the analytical techniques. High accuracy and fast convergence make the proposed methods ideal for the prediction of the circular loop antenna characteristics.