NEURAL MODEL FOR CIRCULAR-SHAPED MICROSHIELD AND CONDUCTOR-BACKED COPLANAR WAVEGUIDE

A Computer Aided Design (CAD) approach based on Artificial Neural Networks (ANN's) is successfully introduced to determine the characteristic parameters of Circular-shaped Microshield and Conductor-backed Coplanar Waveguide (CMCB-CPW). ANN's have been promising tools for many applications and recently ANN has been introduced to microwave modeling, simulation and optimization. The Multi Layered Perceptron (MLP) neural network used in this work were trained with Levenberg-Marquart (LM), Bayesian regularization (BR), Quasi-Newton (QN), Scaled Conjugate gradient (SCG), Conjugate gradient of Fletcher-Powell (CGF) and Conjugate Gradient backpropagation with Polak-Ribiere (CGP) learning algorithms. This has facilitated the usage of ANN models. The notable benefits are simplicity & accurate determination of the characteristic parameters of CMCBCPW's. The greatest advantage is lengthy formulas can be dispensed with.