Neural-Network-Based Multiobjective Optimizer for Dual-Band Circularly Polarized Antenna - Tarek Sallam; Ahmed M. Attiya; Nada Abd El-Latif (Applied Computational Electromagnetics Society Journal (ACES), 2021)
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.