Neural Models for Coplanar Strip Line Synthesis

Abstract

Abstract-Simple and accurate models based on artificial neural networks (ANNs) are presented to accurately determine the physical dimensions of coplanar strip lines (CPSs). Five learning algorithms, Levenberg-Marquardt (LM), bayesian regularization (BR), quasiNewton (QN), conjugate gradient with Fletcher (CGF), and scaled conjugate gradient (SCG), are used to train the neural models. The neural results are compared with the results of the quasi-static analysis and the synthesis formulas available in the literature. The accuracy of the neural model trained by LM algorithm is found to be better than 0.24% for 10614 CPS samples.

Multilayer Perceptron Neural Analysis of Edge Coupled and Conductor-Backed Edge Coupled Coplanar Waveguides

“…ANN learns relationships among sets of input-output data which are characteristics of the device under consideration. It is a very powerful approach for building complex and non-linear relationship between a set of input and output data [15][16][17]. There are many types of neural networks for various applications available in the literature.…”

Neural Model for Circular-Shaped Microshield and Conductor-Backed Coplanar Waveguide

“…The neurons in the input layer only act as buffer for distributing the input signals to neuron in hidden layer. Each neuron in hidden layer sums up its input signal after weighting them and computes it outputs [10,11]. Training a network consists of adjusting its weights using learning algorithms.…”

Simple quasi-static analysis for coplanar stripline within multilayer dielectrics

“…Lots of approaches have been previously introduced to analyze various properties of CPS [1,3,4,5,6,7,8].…”