Synthesis of crossed dipole frequency selective surfaces using genetic algorithms and artificial neural networks

  • Authors:

  • Rossana M. S. Cruz;Paulo H. da F. Silva;Adaildo G. D'Assunção

  • Affiliations:

  • Federal University of Rio Grande do Norte, Natal, RN, Brazil;Federal Center of Technological Education of Paraiba, João Pessoa, PB, Brazil;Electrical Engineering Department, Federal University of Rio Grande do Norte, Natal, RN, Brazil

  • Venue:
  • IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
  • Year:
  • 2009

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Abstract

This work presents the synthesis of crossed dipole freq uency selective surfaces (FSSs) using a genetic algorithm (GA) whose fitness function is composed by an artificial neural network (ANN). The ANN model was trained by the Resilient Backpropagation (RPROP) algorithm, through the use of accurate data provided by a parametric study developed to investigate some of the geometric parameters of the FSSs. The founded advantages in the design of FSS devices using this optimization technique are discussed and the results are compared to those obtained with simulations using the Ansoft Designer™ commercial software, which is based on the method of moments (MoM).