Neural models for coplanar strip line synthesis


Creative Commons License

Yildiz C. , Guney K. , Turkmen M. , Kaya S.

PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER, vol.69, pp.127-144, 2007 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 69
  • Publication Date: 2007
  • Doi Number: 10.2528/pier06120802
  • Title of Journal : PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER
  • Page Numbers: pp.127-144

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), quasi-Newton (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.