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3-D CST Microwave Studio-based Neural Network Characterization and Particle Swarm Optimization of Minkowski Reflectarray in use Microspacecraft applications

dc.contributor.authorGunes, Filiz
dc.contributor.authorDemirel, Salih
dc.contributor.authorNesil, Selahattin
dc.date.accessioned2026-06-27T13:27:29Z
dc.date.issued2013
dc.description.abstractIn this study, the reflection phase characterization of Reflectarray Antenna is established as a highly nonlinear function within the continuous domain of the element geometry and substrate parameters within the defined bandwidth centered the resonant frequency employing the 3-D Computer Simulation Technology Microwave Studio (CST MWS) -based Multi-Layer Perceptron Neural Network (MLP NN). Thus, the 4- dimensional Minkowski space is mapped into the one- dimensional reflection phase space by this MLP NN Black-box analysis model. Furthermore, this analysis model will be used in the Particle Swarm Optimization (PSO) process to determine the optimum substrate thickness and geometrical parameters of the Minkowski reflectarray. Finally, performance of the illustrated analysis model has been established.en
dc.identifier.endpage455
dc.identifier.isbn978-1-4673-6396-9; 978-1-4673-6395-2
dc.identifier.startpage451
dc.identifier.urihttps://hdl.handle.net/20.500.14981/52795
dc.identifier.wos000332043900064
dc.language.isoeng
dc.publisherIEEE
dc.relation.conference6th International Conference on Recent Advances in Space Technologies (RAST)
dc.relation.ispartofPROCEEDINGS OF 6TH INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN SPACE TECHNOLOGIES (RAST 2013)
dc.subjectReflectarray Antenna
dc.subjectH-wall Waveguide
dc.subjectMultilayer Perceptron Neural Network (MLP NN)
dc.subjectBlack-Box
dc.subjectMinkowski Shape
dc.subjectParticle Swarm Optimization
dc.subjectEngineering
dc.title3-D CST Microwave Studio-based Neural Network Characterization and Particle Swarm Optimization of Minkowski Reflectarray in use Microspacecraft applications
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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