Adaptive neuro-fuzzy models for the quasi-static analysis of microstrip line

YILDIZ C., Guney K., TÜRKMEN M., Kaya S.

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, vol.50, no.5, pp.1191-1196, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 50 Issue: 5
  • Publication Date: 2008
  • Doi Number: 10.1002/mop.23322
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1191-1196
  • Keywords: microstrip lines, effective permittivity, characteristic, impedance, adaptive neuro-fuzzy inference system, quasi-static analysis, TRANSMISSION, IDENTIFICATION, COMPUTATION, FREQUENCY, ANTENNAS, SYSTEMS, STRIPS
  • Erciyes University Affiliated: Yes


This article presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the effective permittivities and characteristic impedances of microstrip lines. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It has the advantages of expert knowledge of FISs and learning capability of artificial neural networks. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of AN-FIS. The results of ANFIS are compared with the results of the experimental works, quasi-static methods, and a commercial electromagnetic simulator IE3D. There is very good agreement among the results of ANFIS models and quasi-static methods, IE3D, and experimental works. (C) 2008 Wiley Periodicals, Inc.