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2006-12-21
Neural Models for Coplanar Strip Line Synthesis
By
, Vol. 69, 127-144, 2007
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.
Citation
Celal Yildiz Kerim Guney Mustafa Turkmen Sabri Kaya , "Neural Models for Coplanar Strip Line Synthesis," , Vol. 69, 127-144, 2007.
doi:10.2528/PIER06120802
http://www.jpier.org/PIER/pier.php?paper=06120802
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