Neural-Network-Based Multiobjective Optimizer for Dual-Band Circularly Polarized Antenna

Authors

  • Tarek Sallam Faculty of Electronic and Information Engineering Huaiyin Institute of Technology, Huai'an 223002, Jiangsu, China 2 Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt
  • Ahmed M. Attiya Microwave Engineering Dept., Electronics Research Institute (ERI), Cairo, Egypt
  • Nada Abd El-Latif Microwave Engineering Dept., Electronics Research Institute (ERI), Cairo, Egypt

Keywords:

Circularly polarized antenna, feedforward neural networks, multiobjective optimization

Abstract

A multiobjective optimization (MOO) technique for a dual-band circularly polarized antenna by using neural networks (NNs) is introduced in this paper. In particular, the optimum antenna dimensions are computed by modeling the problem as a multilayer feedforward neural network (FFNN), which is two-stage trained with I/O pairs. The FFNN is chosen because of its characteristic of accurate approximation and good generalization. The data for FFNN training is obtained by using HFSS EM simulator by varying different geometrical parameters of the antenna. A two strip-loaded circular aperture antenna is utilized to demonstrate the optimization technique. The target dual bands are 835– 865 MHz and 2.3–2.35 GHz.

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Published

2021-03-08

How to Cite

[1]
Tarek Sallam, Ahmed M. Attiya, and Nada Abd El-Latif, “Neural-Network-Based Multiobjective Optimizer for Dual-Band Circularly Polarized Antenna”, ACES Journal, vol. 36, no. 3, pp. 252–258, Mar. 2021.

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