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Bandwidth enhancement of patch antennas using neural network dependent modified optimizer

机译:使用依赖神经网络的改进优化器增强贴片天线的带宽

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摘要

Since a conventional microstrip patch antenna is inherently a narrowband radiator, stacked-patch antennas are commonly used either to enhance the bandwidth or to achieve multi-band characteristics. However, the stacked patch structure has a number of geometrical variables which need to be optimized to achieve the desired characteristics. The conventional design procedure involves repeated costly and time-consuming simulations on an electromagnetic simulator to optimize the various geometrical parameters to arrive at the desired radiation characteristics. In this paper, the task of stacked patch antenna design has been approached as an optimization problem. In order to make a faster CAD module for the stacked-antenna design problem, the simulator has been replaced by a trained artificial neural network (ANN) and embedded in a particle swarm optimization algorithm (PSOA). The ANN is helpful in constructing the function mapping black-box, which can relate the frequencies and associated bandwidths of the antenna with its dimensional parameters. The role of the PSOA is to decide the geometrical parameters of the antenna, in response to the designer-specified frequencies and bandwidths. In order to validate the authenticity of the proposed method, a number of antennas have been designed, fabricated, and tested in the laboratory. Simulated and measured results have been compared which establish the accuracy of the proposed technique.
机译:由于常规的微带贴片天线本质上是窄带辐射器,因此堆叠贴片天线通常用于增强带宽或实现多频带特性。但是,堆叠的贴片结构具有许多几何变量,需要对其进行优化以实现所需的特性。常规设计过程涉及在电磁模拟器上重复进行昂贵且费时的模拟,以优化各种几何参数以获得所需的辐射特性。在本文中,已将堆叠贴片天线设计的任务作为优化问题来解决。为了为堆叠天线设计问题提供更快的CAD模块,模拟器已被训练有素的人工神经网络(ANN)取代,并嵌入粒子群优化算法(PSOA)中。人工神经网络有助于构建功能映射黑匣子,该函数可以将天线的频率和相关带宽与其尺寸参数相关联。 PSOA的作用是响应设计人员指定的频率和带宽,确定天线的几何参数。为了验证所提出方法的真实性,已经在实验室中设计,制造和测试了许多天线。仿真和测量结果进行了比较,从而确定了所提出技术的准确性。

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