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首页> 外文期刊>International journal of RF and microwave computer-aided engineering >Giuseppe Peano and Cantor set fractals based miniaturized hybrid fractal antenna for biomedical applications using artificial neural network and firefly algorithm
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Giuseppe Peano and Cantor set fractals based miniaturized hybrid fractal antenna for biomedical applications using artificial neural network and firefly algorithm

机译：Giuseppe Peano和Cantor使用人工神经网络和萤火虫算法在生物医学应用中基于分形的小型混合分形天线

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摘要

In this research paper, Giuseppe Peano and Cantor set fractals based miniaturizedhybrid fractal antenna (GCHFA) is proposed that operates for biomedical applications.The proposed GCHFA is designed by merging Giuseppe Peano and Cantorset fractals that help in achieving better performance characteristics as well as miniaturization.Firefly algorithm (FA) has been employed to optimize the feed positionof the designed antenna. The substrate material selected for the proposed GCHFAis low-cost, commercially available FR4 epoxy whose thickness is 1.6 mm and relativepermittivity is 4.4. A data set of 65 GCHFAs with different geometricalparameters is generated for the realization of two different bioinspired approaches.For the performance evaluation of fabricated prototype, vector network analyzer isused. The experimentally observed resonant frequencies are 2.4400 and5.8115 GHz, and at these resonant frequencies, S (1,1) < −10 dB. The designedantenna is suitable for Industrial, Scientific, and Medical bands of biomedicalapplications. Moreover, the behavior of the proposed GCHFA is nearly omnidirectional.A comparative study of three different artificial neural networks (ANNs) isalso done to evaluate the most suitable ANN type for the analysis of proposedGCHFA. The optimized, simulated, and experimental results depict that they areclosely matched.
机译：在这篇论文中，Giuseppe Peano和Cantor提出了基于分形的微型混合分形天线（GCHFA），该天线可用于生物医学应用;该GCHFA是通过合并Giuseppe Peano和Cantorset分形而设计的，有助于实现更好的性能特征和小型化。 Firefly算法（FA）已用于优化设计天线的馈电位置。为拟议的GCHFA选择的基材材料是低成本的市售FR4环氧树脂，其厚度为1.6毫米，相对介电常数为4.4。为实现两种不同的生物启发方法，生成了具有不同几何参数的65个GCHFA的数据集。为评估所制造原型的性能，使用了矢量网络分析仪。实验观察到的谐振频率为2.4400和5.8115 GHz，在这些谐振频率下，S（1,1）<-10 dB。设计天线适用于生物医学应用的工业，科学和医学频段。此外，拟议的GCHFA的行为几乎是全向的。还对三种不同的人工神经网络（ANN）进行了比较研究，以评估最适合于拟议的GCHFA分析的ANN类型。优化，仿真和实验结果表明它们是紧密匹配的。

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