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International Journal of Infrared and Millimeter Waves

, Volume 27, Issue 9, pp 1241–1255 | Cite as

NEURAL MODELS FOR THE BROADSIDE-COUPLED V-SHAPED MICROSHIELD COPLANAR WAVEGUIDES

  • K. Guney
  • C. Yildiz
  • S. Kaya
  • M. Turkmen
Article

Abstract

This article presents a new approach based on multilayered perceptron neural networks (MLPNNs) to calculate the odd-and even-mode characteristic impedances and effective permittivities of the broadside-coupled V-shaped microshield coplanar waveguides (BC-VSMCPWs). Six learning algorithms, bayesian regulation (BR), Levenberg-Marquardt (LM), quasi-Newton (QN), scaled conjugate gradient (SCG), resilient propagation (RP), and conjugate gradient of Fletcher-Powell (CGF), are used to train the MLPNNs. The neural results are in very good agreement with the results reported elsewhere. When the performances of neural models are compared with each other, the best and worst results are obtained from the MLPNNs trained by the BR and CGF algorithms, respectively.

Keywords

Broadside-coupled V-shaped microshield coplanar waveguides characteristic impedance effective permittivity neural network 

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Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  1. 1.Electrical and Electronics Engineering Department, Faculty of EngineeringErciyes UniversityKayseriTurkey

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