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Decision tree classifiers for unmanned aircraft configuration selection

Decision tree classifiers for unmanned aircraft configuration selection This paper aims to investigate the possibility of lowering the time taken during the aircraft design for unmanned aerial vehicles by using machine learning (ML) for the configuration selection phase. In this work, a database of unmanned aircraft is compiled and is proposed that decision tree classifiers (DTC) can understand the relations between mission and operational requirements and the resulting aircraft configuration.Design/methodology/approachThis paper presents a ML-based approach to configuration selection of unmanned aircraft. Multiple DTC are built to predict the overall configuration. The classifiers are trained with a database of 118 unmanned aircraft with 57 characteristics, 47 of which are inputs for the classification problem, and 10 are the desired outputs, such as wing configuration or engine type.FindingsThis paper shows that DTC can be used for the configuration selection of unmanned aircraft with reasonable accuracy, understanding the connections between the different mission requirements and the culminating configuration. The framework is also capable of dealing with incomplete databases, maximizing the available knowledge.Originality/valueThis paper increases the computational usage for the aircraft design while retaining requirements’ traceability and increasing decision awareness. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aircraft Engineering and Aerospace Technology: An International Journal Emerald Publishing

Decision tree classifiers for unmanned aircraft configuration selection

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References (15)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1748-8842
eISSN
1748-8842
DOI
10.1108/aeat-03-2021-0074
Publisher site
See Article on Publisher Site

Abstract

This paper aims to investigate the possibility of lowering the time taken during the aircraft design for unmanned aerial vehicles by using machine learning (ML) for the configuration selection phase. In this work, a database of unmanned aircraft is compiled and is proposed that decision tree classifiers (DTC) can understand the relations between mission and operational requirements and the resulting aircraft configuration.Design/methodology/approachThis paper presents a ML-based approach to configuration selection of unmanned aircraft. Multiple DTC are built to predict the overall configuration. The classifiers are trained with a database of 118 unmanned aircraft with 57 characteristics, 47 of which are inputs for the classification problem, and 10 are the desired outputs, such as wing configuration or engine type.FindingsThis paper shows that DTC can be used for the configuration selection of unmanned aircraft with reasonable accuracy, understanding the connections between the different mission requirements and the culminating configuration. The framework is also capable of dealing with incomplete databases, maximizing the available knowledge.Originality/valueThis paper increases the computational usage for the aircraft design while retaining requirements’ traceability and increasing decision awareness.

Journal

Aircraft Engineering and Aerospace Technology: An International JournalEmerald Publishing

Published: Aug 12, 2021

Keywords: Unmanned aircraft; Aircraft design; Decision tree classifiers; Machine learning

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