Multilayer Perceptron Neural Analysis of Edge Coupled and Conductor-Backed Edge Coupled Coplanar Waveguides
In recent years, Computer Aided Design (CAD) based on Artificial Neural Networks (ANNs) have been introduced for microwave modeling, simulation and optimization. In this paper, the characteristic parameters of edge coupled and conductor-backed edge coupled Coplanar Waveguides have been determined with the use of ANN model. Eight learning algorithms, Levenberg-Marquart (LM), Bayesian Regularization (BR), Quasi-Newton (QN), Scaled Conjugate Gradient (SCG), Conjugate Gradient of Fletcher-Powell (CGF), Resilient Propagation (RP), Conjugate Gradient back- propagation with Polak-Ribiere (CGP) and Gradient Descent (GD) are used to train the Multi- Layer Perceptron Neural Networks (MLPNNs). The results of neural models presented in this paper are compared with the results of Conformal Mapping Technique (CMT). The neural results are in very good agreement with the CMT results. When the performances of neural models are compared with each other, the best results are obtained from the neural networks trained by LM and BR algorithms.