Expendable launch vehicles in the United States currently have a reliability of 92%. The failures that do occur cost millions of dollars in spacecraft replacement, lost revenue, and other expenses. These costs are passed on in higher insurance rates and launch vehicle price. If the launch outcome of the launch vehicles could be better predicted, the overall cost of launching payloads into space would decrease. This study used artificial neural networks to model the overall launch outcome of a launch vehicle so that the results of a launch could be predicted. Two neural network architectures--MLP and fuzzy ARTMAP--were trained on historical launch data of Atlas, Delta, and Titan launch vehicles. The networks were then tested on their ability to generalize to new data. Fuzzy ARTMAP performed slightly better than MLP overall, but neither network can be used during launch countdown today. Future application of the networks in real-time during the vehicle launch countdown will require the use of more launch specific data.
","abstract_html":"<p>Expendable launch vehicles in the United States currently have a reliability of 92%. The failures that do occur cost millions of dollars in spacecraft replacement, lost revenue, and other expenses. These costs are passed on in higher insurance rates and launch vehicle price. If the launch outcome of the launch vehicles could be better predicted, the overall cost of launching payloads into space would decrease. This study used artificial neural networks to model the overall launch outcome of a launch vehicle so that the results of a launch could be predicted. Two neural network architectures--MLP and fuzzy ARTMAP--were trained on historical launch data of Atlas, Delta, and Titan launch vehicles. The networks were then tested on their ability to generalize to new data. Fuzzy ARTMAP performed slightly better than MLP overall, but neither network can be used during launch countdown today. Future application of the networks in real-time during the vehicle launch countdown will require the use of more launch specific data.</p>","abstract_has_math":false,"creators":["Schuck, Jennifer A."],"institution":null,"degree_name":"Master of Science in Human Factors & Systems","degree_level":"Thesis - Open Access","degree_discipline":"Human Factors and Systems","degree_department":null,"school":null,"contributors":["Linda Trocine","Shawn Michael Doherty","Mehmet Sozen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2004,"date_issued":"2004-07-01T07:00:00Z","date_published":"2004-07-01T07:00:00Z","updated_at":"2026-07-27T19:25:29Z","subjects":["launch vehicles","artificial neural networks","Space Vehicles","Systems Engineering and Multidisciplinary Design Optimization"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/db-theses/183","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Linda Trocine","Shawn Michael Doherty","Mehmet Sozen"]},{"key":"dc:creator","label":"Author","values":["Schuck, Jennifer A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Human Factors and Systems"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Human Factors & Systems"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["launch vehicles","artificial neural networks","Space Vehicles","Systems Engineering and Multidisciplinary Design Optimization"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/db-theses/183"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Expendable launch vehicles in the United States currently have a reliability of 92%. The failures that do occur cost millions of dollars in spacecraft replacement, lost revenue, and other expenses. These costs are passed on in higher insurance rates and launch vehicle price. If the launch outcome of the launch vehicles could be better predicted, the overall cost of launching payloads into space would decrease. This study used artificial neural networks to model the overall launch outcome of a launch vehicle so that the results of a launch could be predicted. Two neural network architectures--MLP and fuzzy ARTMAP--were trained on historical launch data of Atlas, Delta, and Titan launch vehicles. The networks were then tested on their ability to generalize to new data. Fuzzy ARTMAP performed slightly better than MLP overall, but neither network can be used during launch countdown today. Future application of the networks in real-time during the vehicle launch countdown will require the use of more launch specific data.
"]},{"key":"dc:title","label":"Title","values":["Modeling Launch Vehicle Success Using Artificial Neural Networks"]}]}],"canonical_facts":{"dc:contributor":["Linda Trocine","Shawn Michael Doherty","Mehmet Sozen"],"dc:creator":["Schuck, Jennifer A."],"dc:description.abstract":["Expendable launch vehicles in the United States currently have a reliability of 92%. The failures that do occur cost millions of dollars in spacecraft replacement, lost revenue, and other expenses. These costs are passed on in higher insurance rates and launch vehicle price. If the launch outcome of the launch vehicles could be better predicted, the overall cost of launching payloads into space would decrease. This study used artificial neural networks to model the overall launch outcome of a launch vehicle so that the results of a launch could be predicted. Two neural network architectures--MLP and fuzzy ARTMAP--were trained on historical launch data of Atlas, Delta, and Titan launch vehicles. The networks were then tested on their ability to generalize to new data. Fuzzy ARTMAP performed slightly better than MLP overall, but neither network can be used during launch countdown today. Future application of the networks in real-time during the vehicle launch countdown will require the use of more launch specific data.
"],"dc:identifier":["https://commons.erau.edu/db-theses/183"],"dc:subject":["launch vehicles","artificial neural networks","Space Vehicles","Systems Engineering and Multidisciplinary Design Optimization"],"dc:title":["Modeling Launch Vehicle Success Using Artificial Neural Networks"],"thesis:degree_discipline":["Human Factors and Systems"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Human Factors & Systems"]},"updated_at":"2026-07-27T19:25:29Z"}