Computation of Resonant Frequency and Gain from Inset Fed Rectangular Shaped Microstrip Patch Antenna Using Deep Neural Network

被引：0
|
作者：
Pal, Shuvro [1 ]
Raya, Methila Biswas [2 ]
Ali, Khaleda [3 ]
机构：
[1] Khulna Univ, Comp Sci & Engn Discipline, Khulna 9208, Bangladesh
[2] Univ Liberal Arts Bangladesh, Dept Elect & Telecommun Engn, Dhaka 1209, Bangladesh
[3] Univ Dhaka, Dept Elect & Elect Engn, Dhaka 1000, Bangladesh
来源：
2019 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL INFORMATION AND COMMUNICATION TECHNOLOGY (EICT) | 2019年
关键词：
inset fed rectangular shaped microstrip patch antenna; deep neural network; root mean squared error; mean absolute percentage error and multilayer perceptron;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a deep neural network to predict the design parameters of an inset-fed rectangular shaped microstrip patch antenna. A multilayer perceptron based deep neural network has been proposed to predict the resonant frequency and gain values of antenna. Using five hidden layers and mini-batch gradient descent model the presented network converged closely with 1.30% and 1.56% mean absolute percentage errors compared with simulated frequency and gain values respectively of validation dataset. Hence the designed model can be used to determine resonant frequency and gain of inset-fed rectangular shaped microstrip patch antenna with any given dimensions.
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页数：6
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