Artificial Neural Networks for Calculating the Characteristic Impedance of Air-Suspended Trapezoidal and Rectangular-Shaped Microshield Lines

Abstract

Neural models for calculating the characteristic impedance of air-suspended trapezoidal and rectangular-shaped microshield lines, based on the multilayered perceptrons (MLPs), are presented. Six learning algorithms, bayesian regulation (BR), Levenberg-Marquardt (LM), quasi-Newton (QN), scaled conjugate gradient (SCG), resilient propagation (RP), and conjugate gradient of Fletcher-Powell (CGF), are used to train the MLPs. The characteristic impedance results obtained by using neural models are in very good agreement with the results available in the literature. When the performances of neural models are compared with each other, the best test result is obtained from the MLPs trained by the BR algorithm.