Neural models for an asymmetric coplanar stripline with an infinitely wide strip

YILDIZ C., Sağıroğlu Ş., Saracoglu O., TÜRKMEN M.

INTERNATIONAL JOURNAL OF ELECTRONICS, vol.90, no.8, pp.509-516, 2003 (SCI-Expanded) identifier identifier


This paper presents a new approach, based on artificial neural networks (ANNs), to determine the characteristic impedance and the effective permittivity of an asymmetric coplanar stripline (ACPS) with an infinitely wide strip. ANNs are trained with five learning algorithms to obtain better performance and faster convergence with simpler structure. The best results for training and test were obtained from the models trained with the Levenberg-Marquardt and the Bayesian regularization algorithms. The results obtained by using the neural model are in very good agreement with the results available in the literature. The neural models presented in this work provide simplicity and accuracy to determine both the parameters of an ACPS. The method is not time consuming and is easily included in a CAD system.