Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/11851
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Topbas, Eren | - |
dc.contributor.author | Karaca, H. Deniz | - |
dc.contributor.author | Yazicioglu, Yigit | - |
dc.contributor.author | Kasnakoglu, Cosku | - |
dc.date.accessioned | 2024-11-10T14:56:02Z | - |
dc.date.available | 2024-11-10T14:56:02Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 1747-7778 | - |
dc.identifier.issn | 1747-7786 | - |
dc.identifier.uri | https://doi.org/10.1080/17477778.2024.2403424 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/11851 | - |
dc.description.abstract | It is vital for pilots to have precise information about missile ranges during air-to-air combat. The Weapon Engagement Zone (WEZ), or Dynamic Launch Zone (DLZ) for air missions, represents these ranges based on the flight conditions of both the launching and target aircraft. Generating accurate, real-time WEZ functions requires high-fidelity simulations under various engagement scenarios. Classical approaches often require numerous simulations due to uncertainty in sample requirements and complexities like nonlinearities. To address this, the study proposes a method that treats WEZ modeling as a computer experiment, using a surrogate model developed through sequential experimental design and deep neural networks (DNN). This iterative approach optimizes the number of simulations needed, minimizing the loss of model accuracy. The method's effectiveness is validated by comparing it to classical factorial designs, showing that the proposed approach achieves similar accuracy with significantly fewer sample points, making it a more efficient solution for WEZ modeling. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis Ltd | en_US |
dc.relation.ispartof | Journal of Simulation | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Weapon engagement zone | en_US |
dc.subject | computer experiments | en_US |
dc.subject | deep neural network | en_US |
dc.subject | design of simulation experiments | en_US |
dc.subject | Space-Filling Designs | en_US |
dc.title | An experimental design-based approach for modelling of weapon engagement zone of an air-to-air missile | en_US |
dc.type | Article | en_US |
dc.type | Article; Early Access | en_US |
dc.department | TOBB ETÜ | en_US |
dc.identifier.wos | WOS:001331114800001 | en_US |
dc.identifier.scopus | 2-s2.0-85206383237 | en_US |
dc.institutionauthor | … | - |
dc.identifier.doi | 10.1080/17477778.2024.2403424 | - |
dc.authorscopusid | 57193888156 | - |
dc.authorscopusid | 57213023641 | - |
dc.authorscopusid | 8683614000 | - |
dc.authorscopusid | 24802064500 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
item.cerifentitytype | Publications | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairetype | Article | - |
item.openairetype | Article; Early Access | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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