Neural model for coplanar waveguide sandwiched between two dielectric substrates

A new approach based on artificial neural networks is successfully introduced to determine the characteristic parameters of a coplanar waveguide (CPW) sandwiched between two dielectric substrates. Neural models were trained with eight different learning algorithms to obtain better performance and faster convergence with a simpler structure. The best results were obtained from the models trained with Levenberg-Marquardt and Bayesian regulation learning algorithms. The results obtained from the neural model are in very good agreement with theoretical and experimental results available in the literature. The presented neural model is valid for both conventional and sandwiched CPWs.