Neural models for coplanar strip line synthesis

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

PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER, cilt.69, ss.127-144, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 69
  • Basım Tarihi: 2007
  • Doi Numarası: 10.2528/pier06120802
  • Dergi Adı: PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.127-144
  • Erciyes Üniversitesi Adresli: Evet

Özet

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.