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Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/3966

Title: Predicting the Frequency Characteristics of Hybrid Meander Systems Using a Feed-Forward Backpropagation Network
Authors: Plonis, Darius
Katkevičius, Andrius
Krukonis, Audrius Krukonis
Šlegerytė, Vaiva
Maskeliūnas, Rytis
Damaševičius, Robertas
Keywords: hybrid meander system
microwave device
receiver antenna
feed-forward backpropagation network
artificial neural network
Issue Date: 2019
Publisher: MDPI
Citation: Plonis, D.; Katkevičius, A.; Krukonis, A.; Šlegerytė, V.; Maskeliūnas, R.; Damaševičius, R. Predicting the Frequency Characteristics of Hybrid Meander Systems Using a Feed-Forward Backpropagation Network. Electronics 2019, 8, 85.
Series/Report no.: 8;1
Abstract: The process of designing microwave devices is difficult and time-consuming because the analytical and numerical methods used in the design process are complex. Therefore, it is necessary to search for new methods that will allow for an acceleration of synthesis and analytic procedures. This is especially important in cases where the procedures of synthesis and analysis have to be repeated many times, until the correct device configuration is found. Artificial neural networks are one of the possible alternatives for the acceleration of the design process. In this paper we present a procedure for analyzing a hybrid meander system (HMS) using the feed-forward backpropagation network (FFBN). We compared the prediction results of the transmission factor S21(f) and the reflection factor S11(f) , obtained using the FFBN, with results obtained using traditional analytical and numerical methods, as well as with experimental results. The comparisons show that prediction results significantly depend on the FFBN structure. In terms of the lowest difference between the characteristics calculated using the method of moments (MoM) and characteristics predicted using the FFBN, the best prediction was achieved using the FFBN with three hidden layers, which included 18 neurons in the first hidden layer, 14 neurons in the second hidden layer, and 2 neurons in the third hidden layer. Differences between the predicted and calculated results did not exceed 7% for the S11(f) parameter and 5% for the S21(f) parameter. The prediction of parameters using the FFBN allowed the analysis procedure to be sped up from hours to minutes. The experimental results correlated with the predicted characteristics.
Description: This article belongs to the Section Microwave and Wireless Communications
URI: http://dspace.vgtu.lt/handle/1/3966
ISSN: 2079-9292
Appears in Collections:Moksliniai straipsniai / Research articles

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