scholarly journals Optimization and improved Bandwidth of Fork shape Microstrip Antenna via Artificial Neural Network

2016 ◽  
Vol 11 (04) ◽  
pp. 07-12
Author(s):  
Pritam Singha Roy ◽  
Shamik Chakraborty
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Microstrip Antenna ◽  
Artificial Neural

scholarly journals ESTIMASI PARAMETER ANTENA MIKROSTRIP MENGGUNAKAN JARINGAN SYARAF TIRUAN

2019 ◽  
Vol 7 (2) ◽  
pp. 182
Author(s):  
Khairi Budayawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resonant Frequency ◽  
Microstrip Antenna ◽  
Backpropagation Algorithm ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Rectangular Microstrip Antenna

The parameters of a rectangular microstrip antenna are intensely determined by the permittivity of the substrate, the thickness of the substrate, and the resonant frequency. Generally, to get the antenna parameters, a complex mathematical formula is needed to solve. For this reason, an intelligent method is offered to determine antenna’s parameters more easily. In this study, an artificial neural network method with backpropagation algorithm is used to overcome the problem. The network is trained using the Levenberg–Marquardt algorithm. The data used were consisting of 80 training data and 15 testing data. The results have shown that the artificial neural network learning method was successfully utilized to calculate the patch length, the patch width, and the feed point of a rectangular microstrip antenna, where the precision of the resonant frequency obtained of 93.33% at an error of ≤ 0.5%, and 100% at an error of ≤ 1%. However, the artificial neural network method with backpropagation algorithm is quite accurate for determining the parameters of rectangular microstrip antennas.Keywords: Artificial neural network, Backpropagation, Microstrip antenna, Resonant frequency

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