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首页> 外文期刊>Applied Computational Electromagnetics Society journal >Neural-Network-Based Multiobjective Optimizer for Dual-Band Circularly Polarized Antenna
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Neural-Network-Based Multiobjective Optimizer for Dual-Band Circularly Polarized Antenna

机译：双带圆极化天线的神经网络的多目标优化器

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摘要

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 feed-forward 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.
机译：本文介绍了通过使用神经网络（NNS）的双带圆极化天线的多目标优化（MOO）技术。特别地，通过将​​问题建模为多层前馈神经网络（FFNN）来计算最佳天线尺寸，这是用I / O对训练的两阶段。选择FFNN，因为它具有精确近似和良好的概率特性。通过改变天线的不同几何参数，通过使用HFSS EM模拟器获得FFNN训练的数据。使用两个带装载的圆形孔径天线来展示优化技术。目标双频带为835-865 MHz和2.3-2.35 GHz。

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