{"id":25644,"date":"2026-04-10T11:52:56","date_gmt":"2026-04-10T11:52:56","guid":{"rendered":"https:\/\/aerospacerepository.org\/?p=25644"},"modified":"2026-04-10T11:52:56","modified_gmt":"2026-04-10T11:52:56","slug":"reduced-scale-generic-future-fighter-aerodynamic-model-using-neuro-fuzzy-hybridized-with-differential-evolution","status":"publish","type":"post","link":"https:\/\/aerospacerepository.org\/index.php\/2026\/04\/10\/reduced-scale-generic-future-fighter-aerodynamic-model-using-neuro-fuzzy-hybridized-with-differential-evolution\/","title":{"rendered":"Reduced-Scale Generic Future Fighter Aerodynamic Model Using Neuro-Fuzzy Hybridized with Differential Evolution"},"content":{"rendered":"\n<p><strong>V. SANT&#8217;ANA; R. LARSON; P. KRUS; R.M. FINZI NETO<\/strong><\/p>\n\n\n\n<p><strong>DOI Number: 10.13009\/EUCASS2023-094<\/strong><\/p>\n\n\n\n<p>Accurate aerodynamic modeling is crucial for understanding and optimizing the behavior of aircraft systems. This work presents a novel approach to developing a low-cost yet high-fidelity aerodynamic model using machine learning techniques and experimental flight data from a reduced-scale Generic Future Fighter (GFF). The proposed model leverages Neuro-Fuzzy combined with Differential Evolution for training the acquired data, while employing a Fuzzy Rule-Based System (FRBS) with Gaussian-shaped membership functions for inputs. By effectively predicting forces and moments based on input variable values, the developed model serves as a tool for system identification specific to the aircraft under investigation. The results shows that the Neuro-Fuzzy has a good adaptability to this kind of identifications.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.eucass.eu\/doi\/EUCASS2023-094.pdf\">Read the full paper here<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p><b>V. SANT&#8217;ANA; R. LARSON; P. KRUS; R.M. FINZI NETO<\/b><\/p>\n<p>DOI Number: 10.13009\/EUCASS2023-094<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3767,3766,993],"tags":[766,664,3956,3957],"class_list":["post-25644","post","type-post","status-publish","format-standard","hentry","category-1-aerodynamics-and-flight-physics","category-1-aerospace-europe-conference-2023","category-events","tag-aerodynamics","tag-flight-mechanics","tag-fuzzy-rule-based-system","tag-machine-learnings","category-3767","category-3766","category-993","description-off"],"_links":{"self":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/25644","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/comments?post=25644"}],"version-history":[{"count":1,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/25644\/revisions"}],"predecessor-version":[{"id":25645,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/25644\/revisions\/25645"}],"wp:attachment":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/media?parent=25644"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/categories?post=25644"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/tags?post=25644"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}