PREDICTION OF RESONANCE FREQUENCY OF APERTURE COUPLED MICROSTRIP ANTENNAS BY ARTIFICIAL NEURAL NETWORK
| Main Author: | Musa Ataş*, İsa Ataş |
|---|---|
| Format: | Article Journal |
| Terbitan: |
, 2016
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/160855 |
| ctrlnum |
160855 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Musa Ataş*, İsa Ataş</creator><date>2016-10-15</date><description>In this study, the simulation model of Aperture-Coupled Micro-Strip Antenna (ACMA) by using Artificial Neural Network (ANN) is proposed. The developed model tries to predict the output resonance frequency of the ACMA according to the input physical parameters of the antenna. ACMA models were designed in High Frequency Structure Simulator (HFSS) software tool that could conduct three dimensional full-wave electromagnetic structure analysis based on Finite Element Method. Main objective is to simulate HFSS model via proposed learning model. Levenberg-Marquardt (LM) is utilized as a learning algorithm. 500 different ACMA models was designed in HFSS tool. Physical dimensions and output operating frequencies of the ACMA models were recorded in order to establish the dataset. Prediction performance of the proposed ANN simulation model was evaluated by 5-fold cross-validation scheme. Overall generalization error was calculated as 3.58 %. Experiments revealed that proposed simulation model operates at least ten thousand times faster than HFSS software. Due to its overwhelming running speed, it was concluded that proposed LM-ANN simulation model can be utilized as a preliminary search tool for optimizing the industrial ACMA models. </description><identifier>https://zenodo.org/record/160855</identifier><identifier>10.5281/zenodo.160855</identifier><identifier>oai:zenodo.org:160855</identifier><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><source>INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5(10) 252-260</source><subject>Prediction model; aperture coupled microstrip antenna; artificial neural network; resonance frequency.</subject><title>PREDICTION OF RESONANCE FREQUENCY OF APERTURE COUPLED MICROSTRIP ANTENNAS BY ARTIFICIAL NEURAL NETWORK</title><type>Journal:Article</type><type>Journal:Article</type><recordID>160855</recordID></dc>
|
| format |
Journal:Article Journal Journal:Journal |
| author |
Musa Ataş*, İsa Ataş |
| title |
PREDICTION OF RESONANCE FREQUENCY OF APERTURE COUPLED MICROSTRIP ANTENNAS BY ARTIFICIAL NEURAL NETWORK |
| publishDate |
2016 |
| topic |
Prediction model aperture coupled microstrip antenna artificial neural network resonance frequency |
| url |
https://zenodo.org/record/160855 |
| contents |
In this study, the simulation model of Aperture-Coupled Micro-Strip Antenna (ACMA) by using Artificial Neural Network (ANN) is proposed. The developed model tries to predict the output resonance frequency of the ACMA according to the input physical parameters of the antenna. ACMA models were designed in High Frequency Structure Simulator (HFSS) software tool that could conduct three dimensional full-wave electromagnetic structure analysis based on Finite Element Method. Main objective is to simulate HFSS model via proposed learning model. Levenberg-Marquardt (LM) is utilized as a learning algorithm. 500 different ACMA models was designed in HFSS tool. Physical dimensions and output operating frequencies of the ACMA models were recorded in order to establish the dataset. Prediction performance of the proposed ANN simulation model was evaluated by 5-fold cross-validation scheme. Overall generalization error was calculated as 3.58 %. Experiments revealed that proposed simulation model operates at least ten thousand times faster than HFSS software. Due to its overwhelming running speed, it was concluded that proposed LM-ANN simulation model can be utilized as a preliminary search tool for optimizing the industrial ACMA models. |
| id |
IOS16997.160855 |
| institution |
ZAIN Publications |
| institution_id |
7213 |
| institution_type |
library:special library |
| library |
Cognizance Journal of Multidisciplinary Studies |
| library_id |
5267 |
| collection |
Cognizance Journal of Multidisciplinary Studies |
| repository_id |
16997 |
| subject_area |
Multidisciplinary |
| city |
Stockholm |
| province |
INTERNASIONAL |
| shared_to_ipusnas_str |
1 |
| repoId |
IOS16997 |
| first_indexed |
2022-06-06T05:10:42Z |
| last_indexed |
2022-06-06T05:10:42Z |
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dc |
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1734904390671663105 |
| score |
17.60897 |