A CAD Approach based on Artificial Neural Networks for Two Layered Substrate Coplanar Waveguides

摘要

In recent years, Computer Aided Design (CAD) based on Artificial Neural Networks (ANNs) has been introduced for microwave modeling simulation and optimization. In this paper, the characteristic parameters of two layered substrate coplanar waveguides have been determined using the new approach based on Multilayer Perceptron (MLP) neural network and Radial Basis Function (RBF) neural network. Five learning algorithms, Levenberg-Marquart (LM), Bayesian Regularization (BR), Quasi-Newton (QN), Scaled Conjugate Gradient (SCG) and Conjugate Gradient of Fletcher-Powell (CGF) are used to train the MLP Neural Networks (MLPNNs). The results of neural models presented in this paper are compared with the results of Conformal Mapping Technique (CMT). When the performances of neural models are compared with each other, the best results are obtained from the RBF neural network and the MLP network trained by LM and BR algorithms. The results from both neural models are in good agreement with the result available in the literature.