Dimension Optimization of Microstrip Patch Antenna in X/Ku Band via Artificial Neural Network

Özkaya, Umut; Seyfi, Levent

doi:10.1016/j.sbspro.2015.06.434

Cited by 50 publications

(20 citation statements)

References 10 publications

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“…For instance, in [28], the authors used ANN to build a smart antenna capable of tracking the angles of arrival of multiple sources. In [29], ANN is used to optimize the dimensions of the microstrip patch antenna. Additionally, in [30], ANN is trained to replace the full-wave analysis to optimize the bandwidth of the dualring 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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“…GBA is used in datasets where prediction results with high accuracy are needed [11]. The use of GBA in multi-parameter problems is increasing day by day due to its high performance values in nonlinear problems [12]. GBA pseudocode is given in Table 1 [12].…”

Section: Gradient Boosting Algorithmmentioning

confidence: 99%

Using Machine Learning Methods to Predict the Effect of Alternative Fuel Mixtures on Exhaust Emissions

BİLBAN¹,

Aydoğan²

2022

European Journal of Science and Technology

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Machine learning is a science that deals with the design and development processes of algorithms that enable data-based learning. Machine learning methods try to find the most suitable model for new data prediction processes by using the past data. In this study, the data obtained from the engine trials with fuel mixtures of 5%, 10%, 15% by volume using 1-Propanol, 2-Propanol, AVGAS and gasoline fuel were used. Obtained data were compared with 100% gasoline values. In the study, a 4-cylinder engine with direct injection and turbocharging was used. With the obtained measurement results, a database was created to be used in machine learning. With the created database, estimation processes were carried out on ANN, GBA, SVM and AB machine learning models. At the end of the study, it was found that the most suitable model for the estimation of CO, CO2, HC, O2 values was ANN with an R 2 value of 0.9999. For the NO value, it was determined that the AB method was used with an R 2 value of 0.9996. In the estimation process of the CO value, GBA and AB methods are other machine learning methods that can be used as they have a higher value than 0.99 R 2 . CO2, HC and O2, and in the output value estimation process, GBA and AB are other methods that can be used instead of ANN as they have a higher value than 0.99 R 2 . It has been found that there is another machine learning method that can be used for NO value estimation, with an AB 0.99 R 2 value.

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“…Microstrip patch antenna has very small thickness as compared to wavelength i.e. & its height is very small with wavelength, usually [6][7]. The length L of the element is typically used for a rectangular patch [1].…”

Section: Design and Parametric Studymentioning

confidence: 99%

Design of Dual-Band Microstrip Patch Antenna with Chair Shape Slot for Wireless Application

Kaur¹,

Parkash²

2017

IJERT

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This paper presents rectangular microstrip patch antenna for wireless applications. The proposed microstrip patch antenna has dual frequency band i.e. S and C. Moreover gain has obtained having range 3.41dBi to 7.93dBi. The antenna is fed by co-axial feed line technique and comprises of Roger RT/duroid 5870.To understand the characteristics of the proposed antenna the parametric study is to be performed. In addition, under the operating band good antenna characteristics observed like radiation pattern, gain and return loss. The proposed antenna is simulated and designed using HFSS.

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Dimension Optimization of Microstrip Patch Antenna in X/Ku Band via Artificial Neural Network

Cited by 50 publications

References 10 publications

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Using Machine Learning Methods to Predict the Effect of Alternative Fuel Mixtures on Exhaust Emissions

Design of Dual-Band Microstrip Patch Antenna with Chair Shape Slot for Wireless Application

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