Machine Learning-Based Evaluation of the Contribution Effectiveness in SoS Missions
This article presents an efficient and comprehensive evaluation method for aircraft contribution rate to system-of-systems (CSS) by using machine learning (ML). It is based on the whole design space rather than some typical points. After collecting data from mission simulation or analysis, this method simply divides the dataset into two groups (success and failure) and then efficiently trains ML models to do the CSS evaluation. Related literature and novelty are first discussed. Then, after introducing theory fundamentals about mission success and success index space, the proposed CSS evaluation approach is given step by step. Furthermore, two system-of-systems (SoS) simulation cases are provided for the method validation: SoS combat penetration mission (military application) and helicopter SoS rescue mission (civil application). In each case, two ML methods, neural network pattern recognition and support vector machine, are utilized to evaluate CSS. New samples were also collected to validate the trained models. Both methods can train ML models for SIS generation in a very short time. Finally, the comparison with other CSS methods is summarized.
History
Author affiliation
College of Science & Engineering EngineeringVersion
- AM (Accepted Manuscript)
Published in
IEEE Systems JournalVolume
17Issue
4Pagination
1 - 12Publisher
Institute of Electrical and Electronics Engineers (IEEE)issn
1932-8184eissn
1937-9234Copyright date
2023Available date
2023-05-12Publisher DOI
Language
enPublisher version
Deposited by
Dr Yuan GaoDeposit date
2024-06-07Rights Retention Statement
- No