Support Vector Regression Analysis for the Design of Feed in a Rectangular Patch Antenna

Ülker, Sadık

doi:10.1109/ismsit.2019.8932929

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…This allocation system reduced computation complexity in the online antenna. In [34], the authors used data collected from a microwave simulator to train SVM to design the feed section of a microstrip patch antenna.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Shahbazi¹,

Byun²

2022

Computers, Materials &Amp; Continua

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Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna. Two base models are used namely: Multilayer Perceptron (MLP) and Support Vector Machines (SVM). To calculate the weights for each model, an optimization algorithm is used to find the optimal weights of the ensemble. Dynamic Group-Based Cooperative Optimizer (DGCO) is employed to search for optimal weight for the base models. The proposed model is compared with three based models and the average ensemble model. The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

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Section: Machine Learning Modelsmentioning

confidence: 99%

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Shahbazi¹,

Byun²

2022

Computers, Materials &Amp; Continua

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“…A different number of neurons may be used depending on the nature of the problem. e less number of neurons will reduce the learning ability of the system [23].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…In the literature, optimization algorithms and ANN are often preferred to estimate the resonant frequency of microstrip patch antennas. SVR [23], adaptive neurofuzzy interaction system (ANFIS) [24], and artificial bee colony (ABC) optimization are also widely used.…”

Section: Introductionmentioning

confidence: 99%

Comparative Regression Analysis for Estimating Resonant Frequency of C-Like Patch Antennas

Özkaya

Yiğit

Seyfi

et al. 2021

Mathematical Problems in Engineering

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This study provides a comparative analysis of regression techniques to estimate the operating frequency of the C-like microstrip antenna. The performance of well-known regression techniques such as linear regression (LR), regression tree (RT), support vector regression (SVR), Gaussian regression (GR), and artificial neural network (ANN) is tested. For this purpose, 160 C-like microstrip antennas are simulated, of which 145 are used for training of regression techniques and 15 for testing. From the evaluated results, it is found that the pure quadratic Gaussian regression (PQGR) technique has the lowest error rates with 0.0109 mean absolute error (MAE), 0.0087 median error (ME), 0.0002 mean squared error (MSE), 0.0156 root mean squared error (RMSE), and 0.5981 average percentage error (APE). As can be seen in the comparative analysis, the PQGR method outperforms other regression methods on simulation and measurement data. Experimental analysis shows that the resonant frequency of the C-like patch antennas can be calculated very close to measurements.

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“…In Reference 128, the resonance magnitude of a rectangular patch antenna with a two‐section feed was predicted using SVR. The patch antenna, which has dimensions of 50.7 × 39.4 mm 2 and an operating frequency of 1.8 GHz, has two feeds of about 20 mm in length.…”

Section: Predicting Antenna Parameters With Machine Learning Modelsmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

104

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This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

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Support Vector Regression Analysis for the Design of Feed in a Rectangular Patch Antenna

Cited by 12 publications

References 15 publications

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Comparative Regression Analysis for Estimating Resonant Frequency of C-Like Patch Antennas

A review on the design and optimization of antennas using machine learning algorithms and techniques

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