A CAD neural analysis for edge coupled coplanar waveguides

Artificial neural networks (ANNs) have been promising tools for many applications. In recent years, a computer-aided design approach based on ANNs has been introduced to microwave modelling, simulation and optimization. In this work, the characteristics parameters of edge coupled coplanar waveguides (CPWs) have been determined with the use of models. These neural models were trained with LM, BR, QN, CGF and SCG learning algorithms. The results have shown that the estimated characteristics parameters are in very good agreement with the computed results by using conformal mapping theory. The LM learning algorithm was found to be the best algorithm among all. As a result, ANN models presented in this work can be used easily, simply and accurately to determine the characteristics parameters of the edge coupled CPWs.