Multilaver perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides


Ubeyli E., Guler İ.

NEUROCOMPUTING, vol.62, pp.349-365, 2004 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 62
  • Publication Date: 2004
  • Doi Number: 10.1016/j.neucom.2004.04.005
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.349-365
  • Keywords: asymmetric coplanar waveguides, quasistatic parameters, conformal mapping, artificial neural networks, multilayer perceptron, training algorithms, Levenberg-Marquardt algorithm, DESIGN INVITED ARTICLE, ALGORITHMS, COMPONENTS, MODELS, LINES
  • Gazi University Affiliated: Yes

Abstract

Artificial neural networks (ANNs) have recently gained attention as fast and flexible vehicles to microwave modeling, simulation, and optimization. In this study, ANNs, based on the multilayer perceptron, were presented for accurate computation of the quasistatic parameters of asymmetric coplanar waveguides (ACPWs). Multilayer perceptron neural networks (MLPNNs) were trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg-Marquardt algorithms to compute the quasistatic parameters, the characteristic impedance and the effective dielectric constant, of the ACPWs. The results of the MLPNNs trained with the Levenberg-Marquardt algorithm for the quasistatic parameters of the ACPWs were in very good agreement with the results available in the literature obtained by using conformal-mapping technique. (C) 2004 Elsevier B.V. All rights reserved.