Neural models for coplanar strip line synthesis
PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER, cilt.69, ss.127-144, 2007 (SCI-Expanded, Scopus)
- 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
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- 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.