Multilayer perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides

Abstract

Artificial neural networks (ANNs) have recently gained attention as fast and flexible vehicles to microwave modeling, simulation, and optimization. In this study, ANNs, based on the multilayer perceptron, were presented for accurate computation of the quasistatic parameters of asymmetric coplanar waveguides (ACPWs). Multilayer perceptron neural networks (MLPNNs) were trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg–Marquardt algorithms to compute the quasistatic parameters, the characteristic impedance and the effective dielectric constant, of the ACPWs. The results of the MLPNNs trained with the Levenberg–Marquardt algorithm for the quasistatic parameters of the ACPWs were in very good agreement with the results available in the literature obtained by using conformal-mapping technique.