Tunnel-based artificial neural network technique to calculate the resonant frequency of a thick-substrate microstrip antenna

2002 ◽  
Vol 34 (6) ◽  
pp. 460-462 ◽  
Author(s):  
Shyam S. Pattnaik ◽  
Dhruba C. Panda ◽  
S. Devi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resonant Frequency ◽  
Microstrip Antenna ◽  
Thick Substrate ◽  
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

scholarly journals An Application of Artificial Neural Network to Compute the Resonant Frequency of E–Shaped Compact Microstrip Antennas

2013 ◽  
Vol 64 (5) ◽  
pp. 317-322 ◽  
Author(s):  
Ali Akdagli ◽  
Abdurrahim Toktas ◽  
Ahmet Kayabasi ◽  
Ibrahim Develi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resonant Frequency ◽  
Microstrip Antennas ◽  
Multilayer Perceptrons ◽  
Ann Model ◽  
Resonant Frequencies ◽  
Average Percentage ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip antennas (ECMAs) is presented in this paper. The resonant frequencies of 144 ECMAs with different dimensions and electrical parameters were firstly determined by using IE3D(tm) software based on the method of moments (MoM), then the ANN model for computing the resonant frequency was built by considering the simulation data. The parameters and respective resonant frequency values of 130 simulated ECMAs were employed for training and the remaining 14 ECMAs were used for testing the model. The computed resonant frequencies for training and testing by ANN were obtained with the average percentage errors (APE) of 0.257% and 0.523%, respectively. The validity and accuracy of the present approach was verified on the measurement results of an ECMA fabricated in this study. Furthermore, the effects of the slots loading method over the resonant frequency were investigated to explain the relationship between the slots and resonant frequency.

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