Design of well-matched Microwave Slot Antenna on a Flat Metal Grounded Plate using Neural Model

Stankovic, Zoran; Doncov, Nebojsa; Stosic, Biljana; Sarevska, Maja; Milovanović, Ivan

doi:10.1109/icest49890.2020.9232898

2020 55th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) 2020

DOI: 10.1109/icest49890.2020.9232898

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Design of well-matched Microwave Slot Antenna on a Flat Metal Grounded Plate using Neural Model

Zoran Stankovic

Nebojsa Doncov

Biljana Stosic

et al.

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2022

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Cited by 2 publications

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Overview of RBF NN and Antenna Systems

Sarevska

2022

Wseas Transactions on Electronics

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Predicting the mechanical behavior of metallic materials, such as stress-strain curves, is important for studying the plastic behavior of materials. This paper intends to investigate machine learning methods’ capacity to predict the aluminum alloy’s stress-strain curves under different temperature levels. Specifically, three machine learning methods (Gaussian process regression (GPR), neural network (NN), and boosted trees (BT) were employed to predict the stress-strain response of Al6061- T6 at different temperatures, including 25°C, 100°C, 200°C, and 300°C. The performance of the studied machine learning methods has been verified using actual strain-stress measurements collected using uniaxial tensile testing on Al6061-T6. Four statistical scores have been adopted to evaluate the prediction accuracy. Results revealed the potential of machine learning methods in predicting strain-stress measurements. Furthermore, results showed that the NN model dominates the other models by providing a prediction with an averaged mean absolute error percentage of 0.213.

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Overview of RBF NN and Antenna Systems

Sarevska

2022

Wseas Transactions on Electronics

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Adaptively Equalized Bandwidth Optimization Model using SCADA-DWWAN based Neural Network

Mudiar

Sarma

Mastorakis

2022

Wseas Transactions on Electronics

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This paper presents the application of Radial Basis Function neural network in antenna array systems and in the estimation of polarization rotation estimation in the ionosphere. Radial Basis Function neural network is used as it satisfies both universal and best approximation property. We present the architecture of the network, as part of the total system. Presented results show low mean error values and very good match between the referent values and gained one, which shows the successfulness of the particular neural network.

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Design of well-matched Microwave Slot Antenna on a Flat Metal Grounded Plate using Neural Model

Cited by 2 publications

References 9 publications

Overview of RBF NN and Antenna Systems

Overview of RBF NN and Antenna Systems

Adaptively Equalized Bandwidth Optimization Model using SCADA-DWWAN based Neural Network

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