Adaptive-network-based fuzzy inference system models for input resistance computation of circular microstrip antennas


Guney K. , Sarikaya N.

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, vol.50, no.5, pp.1253-1261, 2008 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 50 Issue: 5
  • Publication Date: 2008
  • Doi Number: 10.1002/mop.23354
  • Title of Journal : MICROWAVE AND OPTICAL TECHNOLOGY LETTERS
  • Page Numbers: pp.1253-1261

Abstract

A method based on adaptive-network-based fuzzy inference system (ANFIS) for computing the input resistance of circular microstrip antennas (MSAs) is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, genetic, differential evolution, hybrid learning, particle swarm, and simulated annealing, are used to determine optimally the design parameters of the ANFIS. The results of the ANFIS models show better agreement with the experimental results, as compared with the results of previous methods available in the literature. When the performances of ANFIS models are compared with each other, the best result is obtained from the ANFIS model trained by the least-squares algorithm. (C) 2008 Wiley Periodicals, Inc.